diff --git a/README.md b/README.md index 7f3e0eb..04bf501 100644 --- a/README.md +++ b/README.md @@ -26,7 +26,7 @@ DOI

-This curated list contains 420 awesome open-source projects with a total of 190K stars grouped into 23 categories. All projects are ranked by a [project-quality score](https://github.com/best-of-lists/best-of-generator#project-quality-score), which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an [issue](https://github.com/JuDFTteam/best-of-atomistic-machine-learning/issues/new/choose), submit a [pull request](https://github.com/JuDFTteam/best-of-atomistic-machine-learning/pulls), or directly edit the [projects.yaml](https://github.com/JuDFTteam/best-of-atomistic-machine-learning/edit/main/projects.yaml). +This curated list contains 420 awesome open-source projects with a total of 180K stars grouped into 22 categories. All projects are ranked by a [project-quality score](https://github.com/best-of-lists/best-of-generator#project-quality-score), which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an [issue](https://github.com/JuDFTteam/best-of-atomistic-machine-learning/issues/new/choose), submit a [pull request](https://github.com/JuDFTteam/best-of-atomistic-machine-learning/pulls), or directly edit the [projects.yaml](https://github.com/JuDFTteam/best-of-atomistic-machine-learning/edit/main/projects.yaml). The current focus of this list is more on simulation data rather than experimental data, and more on materials rather than drug design. Nevertheless, contributions from other fields are warmly welcome! @@ -37,7 +37,6 @@ The current focus of this list is more on simulation data rather than experiment ## Contents - [Active learning](#active-learning) _6 projects_ -- [Biomolecules](#biomolecules) _2 projects_ - [Community resources](#community-resources) _30 projects_ - [Datasets](#datasets) _45 projects_ - [Data Structures](#data-structures) _4 projects_ @@ -127,30 +126,6 @@ _Projects that focus on enabling active learning, iterative learning schemes for
-## Biomolecules - -Back to top - -_Projects that focus on biomolecules, protein structure, protein folding, etc. using atomistic ML._ - -
AlphaFold (🥇23 · ⭐ 12K) - Open source code for AlphaFold. Apache-2 - -- [GitHub](https://github.com/google-deepmind/alphafold) (👨‍💻 20 · 🔀 2.1K · 📦 15 · 📋 850 - 29% open · ⏱️ 08.05.2024): - - ``` - git clone https://github.com/google-deepmind/alphafold - ``` -
-
Uni-Fold (🥉16 · ⭐ 370 · 💤) - An open-source platform for developing protein models beyond AlphaFold. Apache-2 - -- [GitHub](https://github.com/dptech-corp/Uni-Fold) (👨‍💻 7 · 🔀 67 · 📥 3.6K · 📋 70 - 28% open · ⏱️ 08.01.2024): - - ``` - git clone https://github.com/dptech-corp/Uni-Fold - ``` -
-
- ## Community resources Back to top @@ -213,9 +188,9 @@ _Projects that collect atomistic ML resources or foster communication within com git clone https://github.com/GT4SD/gt4sd-core ``` -
MatBench Discovery (🥈16 · ⭐ 82 · 📉) - An evaluation framework for machine learning models simulating high-throughput materials discovery. MIT datasets benchmarking model-repository +
MatBench Discovery (🥈16 · ⭐ 82) - An evaluation framework for machine learning models simulating high-throughput materials discovery. MIT datasets benchmarking model-repository -- [GitHub](https://github.com/janosh/matbench-discovery) (👨‍💻 7 · 🔀 11 · 📦 2 · 📋 35 - 8% open · ⏱️ 18.08.2024): +- [GitHub](https://github.com/janosh/matbench-discovery) (👨‍💻 7 · 🔀 11 · 📦 2 · 📋 35 - 5% open · ⏱️ 19.08.2024): ``` git clone https://github.com/janosh/matbench-discovery @@ -233,7 +208,7 @@ _Projects that collect atomistic ML resources or foster communication within com git clone https://github.com/divelab/AIRS ```
-
Neural-Network-Models-for-Chemistry (🥈11 · ⭐ 64) - A collection of Nerual Network Models for chemistry. Unlicensed rep-learn +
Neural-Network-Models-for-Chemistry (🥈11 · ⭐ 65) - A collection of Nerual Network Models for chemistry. Unlicensed rep-learn - [GitHub](https://github.com/Eipgen/Neural-Network-Models-for-Chemistry) (👨‍💻 3 · 🔀 8 · 📋 2 - 50% open · ⏱️ 08.08.2024): @@ -381,7 +356,7 @@ _Datasets, databases and trained models for atomistic ML._ 🔗 ZINC20 - A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable.. graph biomolecules -
OPTIMADE Python tools (🥇26 · ⭐ 64 · 📈) - Tools for implementing and consuming OPTIMADE APIs in Python. MIT +
OPTIMADE Python tools (🥇26 · ⭐ 64) - Tools for implementing and consuming OPTIMADE APIs in Python. MIT - [GitHub](https://github.com/Materials-Consortia/optimade-python-tools) (👨‍💻 28 · 🔀 42 · 📦 48 · 📋 450 - 22% open · ⏱️ 19.08.2024): @@ -817,7 +792,7 @@ _Projects that focus on explainability and model interpretability in atomistic M pip install exmol ```
-
MEGAN: Multi Explanation Graph Attention Student (🥉6 · ⭐ 5 · 📈) - Minimal implementation of graph attention student model architecture. MIT rep-learn +
MEGAN: Multi Explanation Graph Attention Student (🥉6 · ⭐ 5) - Minimal implementation of graph attention student model architecture. MIT rep-learn - [GitHub](https://github.com/aimat-lab/graph_attention_student) (👨‍💻 2 · 🔀 1 · ⏱️ 19.08.2024): @@ -923,7 +898,7 @@ _General tools for atomistic machine learning._
MAML (🥈25 · ⭐ 350) - Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc. BSD-3 -- [GitHub](https://github.com/materialsvirtuallab/maml) (👨‍💻 32 · 🔀 74 · 📦 10 · 📋 70 - 12% open · ⏱️ 03.07.2024): +- [GitHub](https://github.com/materialsvirtuallab/maml) (👨‍💻 32 · 🔀 75 · 📦 10 · 📋 70 - 12% open · ⏱️ 03.07.2024): ``` git clone https://github.com/materialsvirtuallab/maml @@ -957,7 +932,7 @@ _General tools for atomistic machine learning._ git clone https://github.com/uw-cmg/MAST-ML ```
-
Scikit-Matter (🥈19 · ⭐ 73 · 📈) - A collection of scikit-learn compatible utilities that implement methods born out of the materials science and.. BSD-3 scikit-learn +
Scikit-Matter (🥈19 · ⭐ 73) - A collection of scikit-learn compatible utilities that implement methods born out of the materials science and.. BSD-3 scikit-learn - [GitHub](https://github.com/scikit-learn-contrib/scikit-matter) (👨‍💻 15 · 🔀 20 · 📦 10 · 📋 70 - 20% open · ⏱️ 06.08.2024): @@ -985,7 +960,7 @@ _General tools for atomistic machine learning._ pip install xenonpy ```
-
MLatom (🥉13 · ⭐ 36 · 📈) - AI-enhanced computational chemistry. MIT UIP ML-IAP MD ML-DFT ML-ESM transfer-learning active-learning spectroscopy structure-optimization +
MLatom (🥉13 · ⭐ 36) - AI-enhanced computational chemistry. MIT UIP ML-IAP MD ML-DFT ML-ESM transfer-learning active-learning spectroscopy structure-optimization - [GitHub](https://github.com/dralgroup/mlatom) (👨‍💻 3 · 🔀 6 · ⏱️ 24.07.2024): @@ -1573,7 +1548,7 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce
ChemCrow (🥈16 · ⭐ 560) - Open source package for the accurate solution of reasoning-intensive chemical tasks. MIT ai-agent -- [GitHub](https://github.com/ur-whitelab/chemcrow-public) (👨‍💻 3 · 🔀 77 · 📦 4 · 📋 19 - 26% open · ⏱️ 27.03.2024): +- [GitHub](https://github.com/ur-whitelab/chemcrow-public) (👨‍💻 3 · 🔀 80 · 📦 4 · 📋 20 - 30% open · ⏱️ 27.03.2024): ``` git clone https://github.com/ur-whitelab/chemcrow-public @@ -1607,7 +1582,7 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce pip install chatmof ```
-
LLaMP (🥉10 · ⭐ 51) - A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials informatics. An.. BSD-3 materials-discovery cheminformatics generative MD multimodal language-models Python general-tool +
LLaMP (🥉10 · ⭐ 52) - A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials informatics. An.. BSD-3 materials-discovery cheminformatics generative MD multimodal language-models Python general-tool - [GitHub](https://github.com/chiang-yuan/llamp) (👨‍💻 5 · 🔀 7 · 📋 25 - 32% open · ⏱️ 01.08.2024): @@ -1776,7 +1751,7 @@ _Projects that implement mathematical objects used in atomistic machine learning
KFAC-JAX (🥇19 · ⭐ 220) - Second Order Optimization and Curvature Estimation with K-FAC in JAX. Apache-2 -- [GitHub](https://github.com/google-deepmind/kfac-jax) (👨‍💻 13 · 🔀 16 · 📦 10 · 📋 18 - 50% open · ⏱️ 16.08.2024): +- [GitHub](https://github.com/google-deepmind/kfac-jax) (👨‍💻 13 · 🔀 16 · 📦 10 · 📋 18 - 50% open · ⏱️ 19.08.2024): ``` git clone https://github.com/google-deepmind/kfac-jax @@ -1786,7 +1761,7 @@ _Projects that implement mathematical objects used in atomistic machine learning pip install kfac-jax ```
-
gpax (🥇18 · ⭐ 200 · 📈) - Gaussian Processes for Experimental Sciences. MIT probabilistic active-learning +
gpax (🥇18 · ⭐ 200) - Gaussian Processes for Experimental Sciences. MIT probabilistic active-learning - [GitHub](https://github.com/ziatdinovmax/gpax) (👨‍💻 6 · 🔀 22 · 📦 1 · 📋 40 - 20% open · ⏱️ 21.05.2024): @@ -1800,7 +1775,7 @@ _Projects that implement mathematical objects used in atomistic machine learning
SpheriCart (🥈16 · ⭐ 63) - Multi-language library for the calculation of spherical harmonics in Cartesian coordinates. MIT -- [GitHub](https://github.com/lab-cosmo/sphericart) (👨‍💻 10 · 🔀 10 · 📥 60 · 📦 3 · 📋 32 - 59% open · ⏱️ 18.08.2024): +- [GitHub](https://github.com/lab-cosmo/sphericart) (👨‍💻 10 · 🔀 10 · 📥 60 · 📦 3 · 📋 32 - 59% open · ⏱️ 19.08.2024): ``` git clone https://github.com/lab-cosmo/sphericart @@ -1927,7 +1902,7 @@ _Projects that simplify the integration of molecular dynamics and atomistic mach
pair_allegro (🥉9 · ⭐ 34) - LAMMPS pair style for Allegro deep learning interatomic potentials with parallelization support. MIT ML-IAP rep-learn -- [GitHub](https://github.com/mir-group/pair_allegro) (👨‍💻 2 · 🔀 8 · 📋 28 - 35% open · ⏱️ 05.06.2024): +- [GitHub](https://github.com/mir-group/pair_allegro) (👨‍💻 2 · 🔀 8 · 📋 29 - 37% open · ⏱️ 05.06.2024): ``` git clone https://github.com/mir-group/pair_allegro diff --git a/history/2024-08-19_changes.md b/history/2024-08-19_changes.md index 5b69071..f4397e2 100644 --- a/history/2024-08-19_changes.md +++ b/history/2024-08-19_changes.md @@ -1,16 +1 @@ -## 📈 Trending Up - -_Projects that have a higher project-quality score compared to the last update. There might be a variety of reasons, such as increased downloads or code activity._ - -- OPTIMADE Python tools (🥇26 · ⭐ 64 · 📈) - Tools for implementing and consuming OPTIMADE APIs in Python. MIT -- Scikit-Matter (🥈19 · ⭐ 73 · 📈) - A collection of scikit-learn compatible utilities that implement methods born out of the materials science and.. BSD-3 scikit-learn -- gpax (🥇18 · ⭐ 200 · 📈) - Gaussian Processes for Experimental Sciences. MIT probabilistic active-learning -- MLatom (🥉13 · ⭐ 36 · 📈) - AI-enhanced computational chemistry. MIT UIP ML-IAP MD ML-DFT ML-ESM transfer-learning active-learning spectroscopy structure-optimization -- MEGAN: Multi Explanation Graph Attention Student (🥉6 · ⭐ 5 · 📈) - Minimal implementation of graph attention student model architecture. MIT rep-learn - -## 📉 Trending Down - -_Projects that have a lower project-quality score compared to the last update. There might be a variety of reasons such as decreased downloads or code activity._ - -- MatBench Discovery (🥈16 · ⭐ 82 · 📉) - An evaluation framework for machine learning models simulating high-throughput materials discovery. MIT datasets benchmarking model-repository - +Nothing changed from last update. \ No newline at end of file diff --git a/history/2024-08-19_projects.csv b/history/2024-08-19_projects.csv index 2a82575..0f33140 100644 --- a/history/2024-08-19_projects.csv +++ b/history/2024-08-19_projects.csv @@ -1,425 +1,423 @@ -,name,resource,category,license,homepage,description,projectrank,show,labels,github_id,github_url,created_at,updated_at,last_commit_pushed_at,commit_count,recent_commit_count,fork_count,watchers_count,pr_count,open_issue_count,closed_issue_count,star_count,latest_stable_release_published_at,latest_stable_release_number,release_count,contributor_count,pypi_id,conda_id,dependent_project_count,github_dependent_project_count,pypi_url,pypi_latest_release_published_at,pypi_dependent_project_count,pypi_monthly_downloads,monthly_downloads,conda_url,conda_latest_release_published_at,conda_total_downloads,projectrank_placing,dockerhub_id,dockerhub_url,dockerhub_latest_release_published_at,dockerhub_stars,dockerhub_pulls,github_release_downloads,updated_github_id,maven_id,maven_url,maven_latest_release_published_at,maven_dependent_project_count,trending,npm_id,npm_url,npm_latest_release_published_at,npm_dependent_project_count,npm_monthly_downloads,gitlab_id,gitlab_url,docs_url,ignore -0,AI for Science Map,True,community,GPL-3.0 license,https://www.air4.science/map,"Interactive mindmap of the AI4Science research field, including atomistic machine learning, including papers,..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -1,Atomic Cluster Expansion,True,community,,https://cortner.github.io/ACEweb/,Atomic Cluster Expansion (ACE) community homepage.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -2,CrystaLLM,True,community,https://materialis.ai/terms.html,https://crystallm.com,Generate a crystal structure from a composition.,0,True,"['language-models', 'generative', 'pretrained', 'transformer']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -3,GAP-ML.org community homepage,True,community,,https://gap-ml.org/,,0,True,['ml-iap'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -4,matsci.org,True,community,,https://matsci.org/,"A community forum for the discussion of anything materials science, with a focus on computational materials science..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -5,Matter Modeling Stack Exchange - Machine Learning,True,community,,https://mattermodeling.stackexchange.com/questions/tagged/machine-learning,"Forum StackExchange, site Matter Modeling, ML-tagged questions.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -6,Alexandria Materials Database,True,datasets,CC-BY-4.0,https://alexandria.icams.rub.de/,"A database of millions of theoretical crystal structures (3D, 2D and 1D) discovered by machine learning accelerated..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -7,Catalysis Hub,True,datasets,,https://www.catalysis-hub.org/,A web-platform for sharing data and software for computational catalysis research!.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -8,Citrination Datasets,True,datasets,MIT,https://citrination.com/,AI-Powered Materials Data Platform. Open Citrination has been decommissioned.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -9,crystals.ai,True,datasets,,https://crystals.ai/,Curated datasets for reproducible AI in materials science.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -10,DeepChem Models,True,datasets,,https://huggingface.co/DeepChem,DeepChem models on HuggingFace.,0,True,"['model-repository', 'pretrained', 'language-models']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -11,Graphs of Materials Project 20190401,True,datasets,MIT,https://figshare.com/articles/dataset/Graphs_of_Materials_Project_20190401/8097992,The dataset used to train the MEGNet interatomic potential.,0,True,['ml-iap'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -12,HME21 Dataset,True,datasets,,https://doi.org/10.6084/m9.figshare.19658538,High-temperature multi-element 2021 dataset for the PreFerred Potential (PFP)..,0,True,['uip'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -13,JARVIS-Leaderboard,True,datasets,https://github.com/usnistgov/jarvis_leaderboard/blob/main/LICENSE.rst,https://pages.nist.gov/jarvis_leaderboard/,Explore State-of-the-Art Materials Design Methods: https://www.nature.com/articles/s41524-024-01259-w.,0,True,"['model-repository', 'benchmarking', 'community', 'educational']",usnistgov/jarvis_leaderboard,https://github.com/usnistgov/jarvis_leaderboard,2022-07-15 16:48:33,2024-08-14 17:30:42.000,2024-08-14 04:18:28,823.0,11.0,42.0,5.0,320.0,18.0,2.0,56.0,2024-05-16 16:20:41.000,2024.4.26,28.0,33.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -14,Materials Project - Charge Densities,True,datasets,,https://materialsproject.org/ml/charge_densities,Materials Project has started offering charge density information available for download via their public API.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -15,Materials Project Trajectory (MPtrj) Dataset,True,datasets,MIT,https://figshare.com/articles/dataset/Materials_Project_Trjectory_MPtrj_Dataset/23713842,The dataset used to train the CHGNet universal potential.,0,True,['uip'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -16,matterverse.ai,True,datasets,,https://matterverse.ai/,Database of yet-to-be-sythesized materials predicted using state-of-the-art machine learning algorithms.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -17,MPF.2021.2.8,True,datasets,,https://figshare.com/articles/dataset/MPF_2021_2_8/19470599,The dataset used to train the M3GNet universal potential.,0,True,['uip'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -18,NRELMatDB,True,datasets,,https://materials.nrel.gov/,"Computational materials database with the specific focus on materials for renewable energy applications including, but..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -19,Quantum-Machine.org Datasets,True,datasets,,http://quantum-machine.org/datasets/,"Collection of datasets, including QM7, QM9, etc. MD, DFT. Small organic molecules, mostly.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -20,sGDML Datasets,True,datasets,,http://sgdml.org/#datasets,"MD17, MD22, DFT datasets.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -21,MoleculeNet,True,datasets,,https://moleculenet.org/,A Benchmark for Molecular Machine Learning.,0,True,['benchmarking'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -22,ZINC15,True,datasets,,https://zinc15.docking.org/,A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable..,0,True,"['graph', 'biomolecules']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -23,ZINC20,True,datasets,,https://zinc.docking.org/,A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable..,0,True,"['graph', 'biomolecules']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -24,AI for Science 101,True,educational,,https://ai4science101.github.io/,,0,True,"['community', 'rep-learn']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -25,AL4MS 2023 workshop tutorials,True,educational,,https://sites.utu.fi/al4ms2023/media-and-tutorials/,,0,True,['active-learning'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -26,Quantum Chemistry in the Age of Machine Learning,True,educational,,https://www.elsevier.com/books-and-journals/book-companion/9780323900492,"Book, 2022.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -27,MatterGen,True,materials-discovery,,https://www.microsoft.com/en-us/research/blog/mattergen-property-guided-materials-design/,A generative model for inorganic materials design https://doi.org/10.48550/arXiv.2312.03687.,0,True,"['generative', 'proprietary']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -28,IKS-PIML,True,ml-dft,,https://rodare.hzdr.de/record/2720,Code and generated data for the paper Inverting the Kohn-Sham equations with physics-informed machine learning..,0,True,"['neural-operator', 'pinn', 'datasets', 'single-paper']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -29,TeaNet,True,uip,,https://doi.org/10.24433/CO.0749085.v1,Universal neural network interatomic potential inspired by iterative electronic relaxations..,0,True,['ml-iap'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -30,PreFerred Potential (PFP),True,uip,,https://www.nature.com/articles/s41467-022-30687-9#code-availability,Universal neural network potential for material discovery https://doi.org/10.1038/s41467-022-30687-9.,0,True,"['ml-iap', 'proprietary']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -31,MatterSim,True,uip,,https://www.microsoft.com/en-us/research/blog/mattersim-a-deep-learning-model-for-materials-under-real-world-conditions/,"A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures https://doi.org/10.48550/arXiv.2405.04967.",0,True,"['ml-iap', 'active-learning', 'proprietary']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -32,Deep Graph Library (DGL),,rep-learn,Apache-2.0,https://github.com/dmlc/dgl,"Python package built to ease deep learning on graph, on top of existing DL frameworks.",39,True,,dmlc/dgl,https://github.com/dmlc/dgl,2018-04-20 14:49:09,2024-08-19 05:52:07.000,2024-08-18 23:21:54,4341.0,231.0,2963.0,172.0,4964.0,512.0,2340.0,13305.0,2024-06-28 00:16:49.000,2.3.0,453.0,295.0,dgl,dglteam/dgl,423.0,283.0,https://pypi.org/project/dgl,2024-05-13 01:10:39.000,140.0,118536.0,123912.0,https://anaconda.org/dglteam/dgl,2024-06-28 00:12:10.120,365571.0,1.0,,,,,,,,,,,,,,,,,,,,, -33,DeepChem,,general-tool,MIT,https://github.com/deepchem/deepchem,"Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology.",36,True,,deepchem/deepchem,https://github.com/deepchem/deepchem,2015-09-24 23:20:28,2024-08-17 01:23:20.000,2024-08-16 16:15:00,10520.0,54.0,1650.0,145.0,2404.0,627.0,1236.0,5356.0,2024-04-03 16:21:23.000,2.8.0,909.0,247.0,deepchem,conda-forge/deepchem,431.0,418.0,https://pypi.org/project/deepchem,2024-08-16 16:21:57.000,13.0,44761.0,47003.0,https://anaconda.org/conda-forge/deepchem,2024-04-05 16:46:45.105,108652.0,1.0,deepchemio/deepchem,https://hub.docker.com/r/deepchemio/deepchem,2024-08-16 16:36:22.904668,5.0,7429.0,,,,,,,,,,,,,,,, -34,RDKit,,general-tool,BSD-3-Clause,https://github.com/rdkit/rdkit,,36,True,['lang-cpp'],rdkit/rdkit,https://github.com/rdkit/rdkit,2013-05-12 06:19:15,2024-08-16 18:16:23.000,2024-08-16 15:11:31,7815.0,79.0,830.0,82.0,3191.0,932.0,2291.0,2567.0,2024-08-07 12:34:25.000,2024.3.5,100.0,228.0,rdkit,rdkit/rdkit,671.0,3.0,https://pypi.org/project/rdkit,2024-08-07 12:34:25.000,668.0,892141.0,913213.0,https://anaconda.org/rdkit/rdkit,2023-06-16 12:54:07.547,2569671.0,1.0,,,,,,1247.0,,,,,,,,,,,,,,, -35,PyG Models,,rep-learn,MIT,https://github.com/pyg-team/pytorch_geometric/tree/master/torch_geometric/nn/models,Representation learning models implemented in PyTorch Geometric.,35,True,['general-ml'],pyg-team/pytorch_geometric,https://github.com/pyg-team/pytorch_geometric,2017-10-06 16:03:03,2024-08-19 10:05:32.000,2024-08-19 10:05:32,7599.0,79.0,3594.0,252.0,3092.0,1012.0,2644.0,20805.0,2024-04-19 11:37:44.000,2.5.3,40.0,513.0,,,6285.0,6285.0,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, -36,e3nn,,rep-learn,MIT,https://github.com/e3nn/e3nn,A modular framework for neural networks with Euclidean symmetry.,28,True,,e3nn/e3nn,https://github.com/e3nn/e3nn,2020-01-31 13:06:42,2024-08-18 17:48:32.000,2024-07-26 23:00:39,2173.0,12.0,138.0,19.0,222.0,23.0,133.0,924.0,2024-07-27 03:01:58.000,0.5.2,29.0,31.0,e3nn,conda-forge/e3nn,285.0,281.0,https://pypi.org/project/e3nn,2022-04-13 19:24:30.000,4.0,241601.0,242350.0,https://anaconda.org/conda-forge/e3nn,2023-06-18 08:41:30.723,20225.0,1.0,,,,,,,,,,,,,,,,,,,,, -37,paper-qa,,language-models,Apache-2.0,https://github.com/Future-House/paper-qa,LLM Chain for answering questions from documents with citations.,27,True,['ai-agent'],whitead/paper-qa,https://github.com/Future-House/paper-qa,2023-02-05 01:07:25,2024-08-14 21:02:09.000,2024-08-14 21:02:03,249.0,17.0,359.0,40.0,149.0,80.0,74.0,3827.0,2024-06-28 07:43:14.000,4.9.0,103.0,18.0,paper-qa,,73.0,65.0,https://pypi.org/project/paper-qa,2024-06-28 07:43:14.000,8.0,8343.0,8343.0,,,,1.0,,,,,,,Future-House/paper-qa,,,,,,,,,,,,,, -38,DeePMD-kit,,ml-iap,LGPL-3.0,https://github.com/deepmodeling/deepmd-kit,A deep learning package for many-body potential energy representation and molecular dynamics.,27,True,['lang-cpp'],deepmodeling/deepmd-kit,https://github.com/deepmodeling/deepmd-kit,2017-12-12 15:23:44,2024-08-18 00:13:12.000,2024-07-06 15:29:41,2533.0,32.0,488.0,47.0,1985.0,84.0,667.0,1430.0,2024-07-03 19:29:34.000,2.2.11,53.0,69.0,deepmd-kit,deepmodeling/deepmd-kit,20.0,16.0,https://pypi.org/project/deepmd-kit,2024-07-27 04:40:19.000,4.0,1539.0,2266.0,https://anaconda.org/deepmodeling/deepmd-kit,2024-04-06 21:22:08.456,1218.0,1.0,deepmodeling/deepmd-kit,https://hub.docker.com/r/deepmodeling/deepmd-kit,2024-07-27 08:24:51.741318,1.0,2714.0,38367.0,,,,,,,,,,,,,,, -39,Matminer,,general-tool,https://github.com/hackingmaterials/matminer/blob/main/LICENSE,https://github.com/hackingmaterials/matminer,Data mining for materials science.,27,True,,hackingmaterials/matminer,https://github.com/hackingmaterials/matminer,2015-09-24 20:37:00,2024-08-19 08:05:56.000,2024-07-30 12:11:47,4157.0,2.0,184.0,29.0,721.0,29.0,197.0,457.0,2024-03-27 14:48:51.000,0.9.2,71.0,54.0,matminer,conda-forge/matminer,363.0,305.0,https://pypi.org/project/matminer,2024-03-27 14:48:51.000,58.0,10605.0,12063.0,https://anaconda.org/conda-forge/matminer,2024-03-28 11:24:38.014,67106.0,1.0,,,,,,,,,,,,,,,,,,,,, -40,DPA-2,,uip,LGPL-3.0,https://github.com/deepmodeling/deepmd-kit,Towards a universal large atomic model for molecular and material simulation https://doi.org/10.48550/arXiv.2312.15492.,26,True,"['ml-iap', 'pretrained', 'workflows', 'datasets']",deepmodeling/deepmd-kit,https://github.com/deepmodeling/deepmd-kit,2017-12-12 15:23:44,2024-08-18 00:13:12.000,2024-07-06 15:29:41,2533.0,32.0,488.0,47.0,1985.0,84.0,667.0,1429.0,2024-07-03 19:22:15.000,2.2.11,49.0,69.0,,,16.0,16.0,,,,,673.0,,,,1.0,,,,,,38367.0,,,,,,,,,,,,,,, -41,JAX-MD,,md,Apache-2.0,https://github.com/jax-md/jax-md,"Differentiable, Hardware Accelerated, Molecular Dynamics.",26,True,,jax-md/jax-md,https://github.com/jax-md/jax-md,2019-05-13 21:03:37,2024-08-02 20:06:09.000,2024-08-02 20:06:09,920.0,30.0,182.0,48.0,169.0,69.0,79.0,1145.0,2023-08-09 23:18:24.000,0.2.8,38.0,33.0,jax-md,,56.0,53.0,https://pypi.org/project/jax-md,2023-08-09 23:18:24.000,3.0,3171.0,3171.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, -42,SchNetPack,,rep-learn,MIT,https://github.com/atomistic-machine-learning/schnetpack,SchNetPack - Deep Neural Networks for Atomistic Systems.,26,True,,atomistic-machine-learning/schnetpack,https://github.com/atomistic-machine-learning/schnetpack,2018-09-03 15:44:35,2024-08-16 12:43:58.000,2024-08-15 11:53:41,1679.0,23.0,209.0,32.0,410.0,5.0,242.0,760.0,2023-09-29 14:32:16.000,2.0.4,10.0,35.0,schnetpack,,88.0,84.0,https://pypi.org/project/schnetpack,2023-09-29 14:32:16.000,4.0,751.0,751.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, -43,cdk,,rep-eng,LGPL-2.1,https://github.com/cdk/cdk,The Chemistry Development Kit.,26,True,"['cheminformatics', 'lang-java']",cdk/cdk,https://github.com/cdk/cdk,2010-05-11 08:30:07,2024-08-09 05:46:33.000,2024-08-07 15:06:18,17638.0,51.0,154.0,40.0,813.0,32.0,255.0,485.0,2023-08-21 19:50:47.000,cdk-2.9,20.0,165.0,,,16.0,,,,,,178.0,,,,1.0,,,,,,21272.0,,org.openscience.cdk:cdk-bundle,https://search.maven.org/artifact/org.openscience.cdk/cdk-bundle,2023-08-21 08:05:58,16.0,,,,,,,,,, -44,QUIP,,general-tool,GPL-2.0,https://github.com/libAtoms/QUIP,libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io.,26,True,"['md', 'ml-iap', 'rep-eng', 'lang-fortran']",libAtoms/QUIP,https://github.com/libAtoms/QUIP,2013-07-02 15:21:59,2024-08-17 08:38:24.000,2024-08-15 08:10:51,10864.0,25.0,124.0,26.0,177.0,103.0,358.0,350.0,2023-06-15 19:54:01.129,0.9.14,15.0,85.0,quippy-ase,,45.0,41.0,https://pypi.org/project/quippy-ase,2023-01-15 16:54:03.041,4.0,4160.0,4242.0,,,,2.0,libatomsquip/quip,https://hub.docker.com/r/libatomsquip/quip,2023-04-24 21:25:17.345957,4.0,9905.0,533.0,,,,,,,,,,,,,,, -45,OPTIMADE Python tools,,datasets,MIT,https://github.com/Materials-Consortia/optimade-python-tools,Tools for implementing and consuming OPTIMADE APIs in Python.,26,True,,Materials-Consortia/optimade-python-tools,https://github.com/Materials-Consortia/optimade-python-tools,2018-06-05 21:00:07,2024-08-19 07:43:47.000,2024-08-19 07:43:46,1650.0,43.0,42.0,7.0,1695.0,102.0,348.0,64.0,2024-07-20 12:57:23.000,1.1.1,107.0,28.0,optimade,conda-forge/optimade,52.0,48.0,https://pypi.org/project/optimade,2024-07-20 12:57:23.000,4.0,5421.0,7296.0,https://anaconda.org/conda-forge/optimade,2024-07-20 16:43:01.881,84379.0,1.0,,,,,,,,,,,,1.0,,,,,,,,, -46,JAX-DFT,,ml-dft,Apache-2.0,https://github.com/google-research/google-research/tree/master/jax_dft,This library provides basic building blocks that can construct DFT calculations as a differentiable program.,25,True,,google-research/google-research,https://github.com/google-research/google-research,2018-10-04 18:42:48,2024-08-16 18:10:42.000,2024-08-16 18:10:35,4622.0,108.0,7793.0,748.0,860.0,1422.0,325.0,33643.0,,,,796.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, -47,DScribe,,rep-eng,Apache-2.0,https://github.com/SINGROUP/dscribe,DScribe is a python package for creating machine learning descriptors for atomistic systems.,25,True,,SINGROUP/dscribe,https://github.com/SINGROUP/dscribe,2017-05-08 08:29:51,2024-08-13 20:50:00.000,2024-05-28 18:24:28,1288.0,1.0,86.0,20.0,27.0,12.0,92.0,388.0,2024-05-28 18:22:25.000,2.1.1,32.0,18.0,dscribe,conda-forge/dscribe,228.0,193.0,https://pypi.org/project/dscribe,2024-05-28 18:22:25.000,35.0,127567.0,130020.0,https://anaconda.org/conda-forge/dscribe,2024-05-28 23:16:49.298,120240.0,1.0,,,,,,,,,,,,,,,,,,,,, -48,MAML,,general-tool,BSD-3-Clause,https://github.com/materialsvirtuallab/maml,"Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.",25,True,,materialsvirtuallab/maml,https://github.com/materialsvirtuallab/maml,2020-01-25 15:04:21,2024-08-12 13:32:30.000,2024-07-03 18:08:37,1747.0,48.0,74.0,21.0,580.0,9.0,61.0,346.0,2024-06-13 15:29:41.000,2024.6.13,16.0,32.0,maml,,12.0,10.0,https://pypi.org/project/maml,2024-06-13 15:29:41.000,2.0,192.0,192.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -49,NequIP,,ml-iap,MIT,https://github.com/mir-group/nequip,NequIP is a code for building E(3)-equivariant interatomic potentials.,24,True,,mir-group/nequip,https://github.com/mir-group/nequip,2021-03-15 23:44:39,2024-07-25 22:18:07.000,2024-07-09 15:58:45,1873.0,32.0,126.0,23.0,161.0,22.0,66.0,581.0,2024-07-09 16:05:06.000,0.6.1,16.0,11.0,nequip,conda-forge/nequip,24.0,23.0,https://pypi.org/project/nequip,2024-07-09 16:05:26.000,1.0,2571.0,2768.0,https://anaconda.org/conda-forge/nequip,2024-07-10 05:13:00.157,5335.0,1.0,,,,,,,,,,,,,,,,,,,,, -50,AlphaFold,,biomolecules,Apache-2.0,https://github.com/google-deepmind/alphafold,Open source code for AlphaFold.,23,True,,google-deepmind/alphafold,https://github.com/google-deepmind/alphafold,2021-06-17 14:06:06,2024-06-27 04:19:33.000,2024-05-08 14:04:54,143.0,,2090.0,222.0,104.0,250.0,601.0,12214.0,2023-04-05 09:45:53.000,2.3.2,13.0,20.0,,,15.0,15.0,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, -51,dgl-lifesci,,rep-learn,Apache-2.0,https://github.com/awslabs/dgl-lifesci,Python package for graph neural networks in chemistry and biology.,23,False,,awslabs/dgl-lifesci,https://github.com/awslabs/dgl-lifesci,2020-04-23 07:14:21,2023-11-01 19:32:07.000,2023-04-16 03:55:52,236.0,,140.0,17.0,141.0,26.0,57.0,704.0,2023-02-13 08:45:17.000,0.3.2,17.0,22.0,dgllife,,229.0,225.0,https://pypi.org/project/dgllife,2022-12-21 13:18:00.570,4.0,33952.0,33952.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, -52,TorchANI,,ml-iap,MIT,https://github.com/aiqm/torchani,Accurate Neural Network Potential on PyTorch.,23,True,,aiqm/torchani,https://github.com/aiqm/torchani,2018-04-02 15:43:04,2024-07-19 17:54:52.000,2023-11-14 16:32:59,434.0,,124.0,31.0,483.0,24.0,144.0,455.0,2023-11-14 16:41:14.000,2.2.4,24.0,19.0,torchani,conda-forge/torchani,43.0,39.0,https://pypi.org/project/torchani,2023-11-14 16:41:14.000,4.0,3596.0,12142.0,https://anaconda.org/conda-forge/torchani,2024-05-31 06:36:10.067,410253.0,1.0,,,,,,,,,,,,,,,,,,,,, -53,TorchMD-NET,,ml-iap,MIT,https://github.com/torchmd/torchmd-net,Training neural network potentials.,23,True,"['md', 'rep-learn', 'transformer', 'pretrained']",torchmd/torchmd-net,https://github.com/torchmd/torchmd-net,2021-04-09 16:16:32,2024-08-16 08:57:15.000,2024-08-16 08:56:00,1286.0,70.0,69.0,11.0,229.0,34.0,84.0,309.0,2024-08-16 08:57:15.000,2.4.0,26.0,16.0,,conda-forge/torchmd-net,,,,,,,10794.0,https://anaconda.org/conda-forge/torchmd-net,2024-07-09 18:53:56.674,107945.0,1.0,,,,,,,,,,,,,,,,,,,,, -54,JARVIS-Tools,,general-tool,https://github.com/usnistgov/jarvis/blob/master/LICENSE.rst,https://github.com/usnistgov/jarvis,JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications:..,23,True,,usnistgov/jarvis,https://github.com/usnistgov/jarvis,2017-06-22 19:34:02,2024-08-04 13:38:34.000,2024-06-28 03:35:23,2106.0,1.0,120.0,26.0,235.0,46.0,45.0,294.0,2024-06-28 03:39:12.000,2024.5.10,108.0,15.0,jarvis-tools,conda-forge/jarvis-tools,124.0,93.0,https://pypi.org/project/jarvis-tools,2024-06-28 03:39:12.000,31.0,21399.0,22998.0,https://anaconda.org/conda-forge/jarvis-tools,2024-06-28 03:50:37.135,73568.0,2.0,,,,,,,,,,,,,,,,,,,,, -55,MatGL (Materials Graph Library),,rep-learn,BSD-3-Clause,https://github.com/materialsvirtuallab/matgl,Graph deep learning library for materials.,23,True,['multifidelity'],materialsvirtuallab/matgl,https://github.com/materialsvirtuallab/matgl,2022-08-29 18:36:05,2024-08-17 15:40:17.000,2024-08-17 15:40:16,1029.0,66.0,58.0,12.0,219.0,7.0,85.0,248.0,2024-08-07 12:24:58.000,1.1.3,31.0,17.0,m3gnet,,49.0,44.0,https://pypi.org/project/m3gnet,2022-11-17 23:25:34.805,5.0,626.0,626.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, -56,CHGNet,,uip,https://github.com/CederGroupHub/chgnet/blob/main/LICENSE,https://github.com/CederGroupHub/chgnet,Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov.,23,True,"['ml-iap', 'md', 'pretrained', 'electrostatics', 'magnetism', 'structure-relaxation']",CederGroupHub/chgnet,https://github.com/CederGroupHub/chgnet,2023-02-24 23:44:24,2024-08-18 19:19:11.000,2024-08-18 19:19:11,414.0,20.0,59.0,5.0,91.0,4.0,48.0,219.0,2024-06-05 16:47:28.000,0.3.8,16.0,7.0,chgnet,,44.0,30.0,https://pypi.org/project/chgnet,2024-06-05 16:47:28.000,14.0,28245.0,28245.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -57,dpdata,,data-structures,LGPL-3.0,https://github.com/deepmodeling/dpdata,"Manipulating multiple atomic simulation data formats, including DeePMD-kit, VASP, LAMMPS, ABACUS, etc.",23,True,,deepmodeling/dpdata,https://github.com/deepmodeling/dpdata,2019-04-12 13:24:23,2024-08-13 00:50:09.000,2024-06-06 00:03:38,724.0,6.0,128.0,9.0,477.0,28.0,80.0,195.0,2024-06-06 00:07:03.000,0.2.19,46.0,60.0,dpdata,deepmodeling/dpdata,162.0,122.0,https://pypi.org/project/dpdata,2024-06-06 00:07:03.000,40.0,26057.0,26063.0,https://anaconda.org/deepmodeling/dpdata,2023-09-27 20:07:36.945,218.0,1.0,,,,,,,,,,,,,,,,,,,,, -58,MEGNet,,ml-iap,BSD-3-Clause,https://github.com/materialsvirtuallab/megnet,Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals.,22,False,['multifidelity'],materialsvirtuallab/megnet,https://github.com/materialsvirtuallab/megnet,2018-12-12 21:31:28,2023-04-27 02:39:17.000,2023-04-27 02:39:17,1146.0,,152.0,25.0,314.0,21.0,57.0,494.0,2022-11-16 21:25:01.818,1.3.2,37.0,14.0,megnet,,84.0,80.0,https://pypi.org/project/megnet,2022-11-16 21:25:01.818,4.0,429.0,429.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, -59,MACE,,ml-iap,MIT,https://github.com/ACEsuit/mace,MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.,22,True,,ACEsuit/mace,https://github.com/ACEsuit/mace,2022-06-21 18:44:34,2024-08-18 21:06:02.000,2024-08-12 10:01:02,699.0,58.0,169.0,24.0,221.0,54.0,168.0,449.0,2024-07-16 10:55:30.000,0.3.6,7.0,35.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, -60,GPUMD,,ml-iap,GPL-3.0,https://github.com/brucefan1983/GPUMD,GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials..,22,True,"['md', 'lang-cpp', 'electrostatics']",brucefan1983/GPUMD,https://github.com/brucefan1983/GPUMD,2017-07-14 15:32:56,2024-08-19 05:55:47.000,2024-08-19 05:55:47,3962.0,233.0,112.0,27.0,521.0,23.0,161.0,429.0,2024-08-18 13:16:02.000,3.9.5,42.0,33.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, -61,DP-GEN,,ml-iap,LGPL-3.0,https://github.com/deepmodeling/dpgen,The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field.,22,True,['workflows'],deepmodeling/dpgen,https://github.com/deepmodeling/dpgen,2019-06-13 11:43:56,2024-08-13 00:50:19.000,2024-04-10 06:31:36,2083.0,,172.0,13.0,835.0,35.0,261.0,291.0,2024-04-10 06:37:54.000,0.12.1,18.0,64.0,dpgen,deepmodeling/dpgen,7.0,6.0,https://pypi.org/project/dpgen,2024-04-10 06:37:54.000,1.0,355.0,390.0,https://anaconda.org/deepmodeling/dpgen,2023-06-16 19:27:03.566,204.0,1.0,,,,,,1745.0,,,,,,,,,,,,,,, -62,pymatviz,,visualization,MIT,https://github.com/janosh/pymatviz,A toolkit for visualizations in materials informatics.,22,True,"['general-tool', 'probabilistic']",janosh/pymatviz,https://github.com/janosh/pymatviz,2021-02-21 12:40:34,2024-08-18 06:48:33.000,2024-08-18 06:44:53,327.0,51.0,13.0,7.0,151.0,9.0,32.0,151.0,2024-08-18 06:48:33.000,0.10.1,27.0,7.0,pymatviz,,10.0,8.0,https://pypi.org/project/pymatviz,2024-08-18 06:48:33.000,2.0,2122.0,2122.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, -63,MPContribs,,datasets,MIT,https://github.com/materialsproject/MPContribs,Platform for materials scientists to contribute and disseminate their materials data through Materials Project.,22,True,,materialsproject/MPContribs,https://github.com/materialsproject/MPContribs,2014-12-11 18:25:27,2024-08-19 08:09:58.000,2024-08-12 17:10:17,5576.0,40.0,20.0,10.0,1710.0,21.0,78.0,34.0,2024-06-20 22:37:55.000,5.8.4,162.0,25.0,mpcontribs-client,,41.0,38.0,https://pypi.org/project/mpcontribs-client,2024-06-20 22:37:55.000,3.0,1453.0,1453.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, -64,Best-of Machine Learning with Python,,community,CC-BY-4.0,https://github.com/ml-tooling/best-of-ml-python,A ranked list of awesome machine learning Python libraries. Updated weekly.,21,True,"['general-ml', 'lang-py']",ml-tooling/best-of-ml-python,https://github.com/ml-tooling/best-of-ml-python,2020-11-29 19:41:36,2024-07-22 15:36:22.000,2024-07-22 15:36:22,496.0,9.0,2336.0,409.0,266.0,25.0,34.0,16176.0,2024-06-06 15:57:10.000,2024.06.06,100.0,46.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, -65,NVIDIA Deep Learning Examples for Tensor Cores,,rep-learn,https://github.com/NVIDIA/DeepLearningExamples/blob/master/DGLPyTorch/DrugDiscovery/SE3Transformer/LICENSE,https://github.com/NVIDIA/DeepLearningExamples#graph-neural-networks,State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and..,21,True,"['educational', 'drug-discovery']",NVIDIA/DeepLearningExamples,https://github.com/NVIDIA/DeepLearningExamples,2018-05-02 17:04:05,2024-08-12 14:01:29.000,2024-04-04 13:37:56,1437.0,,3061.0,300.0,538.0,307.0,570.0,13129.0,,,,115.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -66,DIG: Dive into Graphs,,rep-learn,GPL-3.0,https://github.com/divelab/DIG,A library for graph deep learning research.,21,True,,divelab/DIG,https://github.com/divelab/DIG,2020-10-30 03:51:15,2024-07-15 07:18:56.000,2024-02-04 20:37:53,1083.0,,281.0,29.0,41.0,34.0,176.0,1828.0,2023-04-07 20:33:15.000,1.1.0,10.0,50.0,dive-into-graphs,,,,https://pypi.org/project/dive-into-graphs,2022-06-27 05:08:24.000,,411.0,411.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -67,FAIR Chemistry datasets,,datasets,MIT,https://github.com/FAIR-Chem/fairchem,"Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project.",21,True,['catalysis'],FAIR-Chem/fairchem,https://github.com/FAIR-Chem/fairchem,2019-09-26 04:47:27,2024-08-17 07:23:58.000,2024-08-17 01:02:33,825.0,63.0,229.0,24.0,618.0,13.0,187.0,748.0,2024-08-14 16:52:55.000,fairchem_demo_ocpapi-0.1.0,8.0,41.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, -68,FLARE,,active-learning,MIT,https://github.com/mir-group/flare,An open-source Python package for creating fast and accurate interatomic potentials.,21,True,"['lang-cpp', 'ml-iap']",mir-group/flare,https://github.com/mir-group/flare,2018-08-30 23:40:56,2024-08-15 19:57:53.000,2024-08-15 19:57:53,4412.0,15.0,65.0,20.0,187.0,36.0,170.0,283.0,2024-03-25 15:48:12.000,1.3.0,6.0,36.0,,,11.0,11.0,,,,,0.0,,,,1.0,,,,,,7.0,,,,,,,,,,,,,,, -69,e3nn-jax,,rep-learn,Apache-2.0,https://github.com/e3nn/e3nn-jax,jax library for E3 Equivariant Neural Networks.,21,True,,e3nn/e3nn-jax,https://github.com/e3nn/e3nn-jax,2021-06-08 13:21:51,2024-08-14 05:15:15.000,2024-08-14 05:13:07,1000.0,15.0,18.0,12.0,48.0,1.0,19.0,173.0,2024-08-14 05:14:56.000,0.20.7,43.0,7.0,e3nn-jax,,49.0,36.0,https://pypi.org/project/e3nn-jax,2024-08-14 05:15:15.000,13.0,2623.0,2623.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -70,Metatensor,,data-structures,BSD-3-Clause,https://github.com/lab-cosmo/metatensor,Self-describing sparse tensor data format for atomistic machine learning and beyond.,21,True,"['lang-rust', 'lang-c', 'lang-cpp', 'lang-py']",lab-cosmo/metatensor,https://github.com/lab-cosmo/metatensor,2022-03-01 15:58:28,2024-08-15 14:39:40.000,2024-08-15 14:39:40,781.0,74.0,15.0,18.0,514.0,77.0,131.0,47.0,2024-07-15 12:55:26.000,metatensor-torch-v0.5.3,38.0,21.0,,,11.0,11.0,,,,,2441.0,,,,2.0,,,,,,24419.0,,,,,,,,,,,,,,, -71,DM21,,ml-dft,Apache-2.0,https://github.com/google-deepmind/deepmind-research/tree/master/density_functional_approximation_dm21,This package provides a PySCF interface to the DM21 (DeepMind 21) family of exchange-correlation functionals described..,20,False,,google-deepmind/deepmind-research,https://github.com/google-deepmind/deepmind-research,2019-01-15 09:54:13,2024-08-09 16:59:25.000,2023-06-02 17:04:50,369.0,,2541.0,325.0,236.0,240.0,138.0,13017.0,,,,92.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, -72,ALIGNN,,rep-learn,https://github.com/usnistgov/alignn/blob/main/LICENSE.rst,https://github.com/usnistgov/alignn,Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en.,20,True,,usnistgov/alignn,https://github.com/usnistgov/alignn,2021-04-19 20:08:09,2024-07-29 12:39:30.000,2024-07-15 20:09:42,707.0,15.0,78.0,11.0,107.0,34.0,24.0,205.0,2024-06-28 03:31:00.000,2024.5.27,46.0,7.0,alignn,,18.0,14.0,https://pypi.org/project/alignn,2024-06-28 03:31:00.000,4.0,974.0,974.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -73,fairchem,,ml-iap,,https://github.com/FAIR-Chem/fairchem,FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project (ocp).,19,True,"['pretrained', 'rep-learn', 'catalysis']",FAIR-Chem/fairchem,https://github.com/FAIR-Chem/fairchem,2019-09-26 04:47:27,2024-08-17 07:23:58.000,2024-08-17 01:02:33,825.0,63.0,229.0,24.0,618.0,13.0,187.0,748.0,2024-08-14 16:52:55.000,fairchem_demo_ocpapi-0.1.0,8.0,41.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -74,MACE-MP,,uip,MIT,https://github.com/ACEsuit/mace-mp,Pretrained foundation models for materials chemistry.,19,True,"['ml-iap', 'pretrained', 'rep-learn', 'md']",ACEsuit/mace-mp,https://github.com/ACEsuit/mace-mp,2024-01-11 10:55:55,2024-07-16 11:55:19.000,2024-04-24 14:56:12,10.0,,164.0,10.0,1.0,1.0,6.0,425.0,2024-07-16 11:55:19.000,0.3.6,4.0,2.0,mace-torch,,12.0,,https://pypi.org/project/mace-torch,2024-07-16 11:55:19.000,12.0,9716.0,12813.0,,,,2.0,,,,,,21681.0,,,,,,,,,,,,,,, -75,exmol,,xai,MIT,https://github.com/ur-whitelab/exmol,Explainer for black box models that predict molecule properties.,19,True,,ur-whitelab/exmol,https://github.com/ur-whitelab/exmol,2021-08-03 17:56:06,2024-06-02 00:38:18.000,2023-12-04 18:03:57,189.0,,41.0,9.0,77.0,11.0,58.0,278.0,2023-06-19 20:58:01.262,3.0.3,27.0,7.0,exmol,,21.0,20.0,https://pypi.org/project/exmol,2022-06-03 18:52:10.000,1.0,688.0,688.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, -76,KFAC-JAX,,math,Apache-2.0,https://github.com/google-deepmind/kfac-jax,Second Order Optimization and Curvature Estimation with K-FAC in JAX.,19,True,,google-deepmind/kfac-jax,https://github.com/google-deepmind/kfac-jax,2022-03-18 10:19:24,2024-08-16 20:20:54.000,2024-08-16 20:20:49,222.0,22.0,16.0,12.0,243.0,9.0,9.0,225.0,2024-04-04 10:59:13.000,0.0.6,5.0,13.0,kfac-jax,,11.0,10.0,https://pypi.org/project/kfac-jax,2024-04-04 10:59:13.000,1.0,747.0,747.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, -77,Chemiscope,,visualization,BSD-3-Clause,https://github.com/lab-cosmo/chemiscope,An interactive structure/property explorer for materials and molecules.,19,True,['lang-js'],lab-cosmo/chemiscope,https://github.com/lab-cosmo/chemiscope,2019-10-03 09:59:42,2024-08-13 16:40:24.000,2024-08-05 21:35:00,730.0,22.0,29.0,19.0,238.0,36.0,86.0,122.0,2024-06-14 16:42:03.000,0.7.1,16.0,22.0,,,9.0,6.0,,,,,47.0,,,,3.0,,,,,,249.0,,,,,,,chemiscope,https://www.npmjs.com/package/chemiscope,2023-03-15 15:39:26.701,3.0,43.0,,,, -78,MAST-ML,,general-tool,MIT,https://github.com/uw-cmg/MAST-ML,MAterials Simulation Toolkit for Machine Learning (MAST-ML).,19,True,,uw-cmg/MAST-ML,https://github.com/uw-cmg/MAST-ML,2017-02-16 17:03:57,2024-08-05 20:49:39.000,2024-04-17 20:51:19,3296.0,,58.0,14.0,37.0,32.0,191.0,101.0,2024-04-17 21:30:14.000,3.2.0,7.0,18.0,,,43.0,43.0,,,,,2.0,,,,2.0,,,,,,95.0,,,,,,,,,,,,,,, -79,mlcolvar,,md,MIT,https://github.com/luigibonati/mlcolvar,A unified framework for machine learning collective variables for enhanced sampling simulations.,19,True,['enhanced-sampling'],luigibonati/mlcolvar,https://github.com/luigibonati/mlcolvar,2021-09-21 21:32:04,2024-07-31 15:12:33.000,2024-06-14 14:38:42,1089.0,55.0,24.0,7.0,80.0,13.0,57.0,91.0,2024-06-12 17:08:54.000,1.1.1,10.0,8.0,mlcolvar,,2.0,2.0,https://pypi.org/project/mlcolvar,2024-06-12 17:08:54.000,,152.0,152.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -80,MALA,,ml-dft,BSD-3-Clause,https://github.com/mala-project/mala,Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data.,19,True,,mala-project/mala,https://github.com/mala-project/mala,2021-03-31 11:40:38,2024-08-14 11:15:04.000,2024-07-04 09:53:01,2307.0,92.0,23.0,9.0,303.0,37.0,230.0,80.0,2024-02-01 08:57:56.000,1.2.1,9.0,44.0,,,1.0,1.0,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, -81,Scikit-Matter,,general-tool,BSD-3-Clause,https://github.com/scikit-learn-contrib/scikit-matter,A collection of scikit-learn compatible utilities that implement methods born out of the materials science and..,19,True,['scikit-learn'],scikit-learn-contrib/scikit-matter,https://github.com/scikit-learn-contrib/scikit-matter,2020-10-12 19:23:26,2024-08-12 16:25:31.000,2024-08-06 07:51:07,386.0,18.0,20.0,17.0,162.0,14.0,56.0,73.0,2023-08-24 17:18:49.000,0.2.0,7.0,15.0,skmatter,conda-forge/skmatter,10.0,10.0,https://pypi.org/project/skmatter,2023-08-24 17:18:19.000,,990.0,1103.0,https://anaconda.org/conda-forge/skmatter,2023-08-24 19:08:29.551,1932.0,2.0,,,,,,,,,,,,1.0,,,,,,,,, -82,ZnDraw,,visualization,EPL-2.0,https://github.com/zincware/ZnDraw,"A powerful tool for visualizing, modifying, and analysing atomistic systems.",19,True,"['md', 'generative', 'lang-js']",zincware/ZnDraw,https://github.com/zincware/ZnDraw,2023-04-12 15:01:21,2024-08-19 08:48:54.000,2024-08-14 07:17:45,381.0,81.0,2.0,1.0,341.0,100.0,208.0,29.0,2024-08-14 07:17:12.000,0.4.6,55.0,7.0,zndraw,,5.0,3.0,https://pypi.org/project/zndraw,2024-08-14 07:18:10.000,2.0,1385.0,1385.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, -83,Graph-based Deep Learning Literature,,community,MIT,https://github.com/naganandy/graph-based-deep-learning-literature,links to conference publications in graph-based deep learning.,18,True,"['general-ml', 'rep-learn']",naganandy/graph-based-deep-learning-literature,https://github.com/naganandy/graph-based-deep-learning-literature,2017-12-01 14:48:35,2024-08-17 13:14:37.000,2024-08-17 13:14:32,7716.0,36.0,737.0,247.0,21.0,,14.0,4715.0,,,,12.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, -84,Uni-Mol,,rep-learn,MIT,https://github.com/deepmodeling/Uni-Mol,Official Repository for the Uni-Mol Series Methods.,18,True,['pretrained'],deepmodeling/Uni-Mol,https://github.com/deepmodeling/Uni-Mol,2022-05-22 13:26:41,2024-08-14 10:40:57.000,2024-08-14 10:40:52,130.0,19.0,117.0,15.0,111.0,60.0,88.0,638.0,2024-07-06 07:05:10.000,0.2.1,3.0,16.0,,,,,,,,,661.0,,,,2.0,,,,,,14542.0,,,,,,,,,,,,,,, -85,GT4SD,,generative,MIT,https://github.com/GT4SD/gt4sd-core,"GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process.",18,True,"['pretrained', 'drug-discovery', 'rep-learn']",GT4SD/gt4sd-core,https://github.com/GT4SD/gt4sd-core,2022-02-11 19:06:58,2024-07-04 11:11:27.000,2024-07-04 11:11:26,296.0,8.0,66.0,17.0,148.0,14.0,98.0,327.0,2024-06-13 15:18:45.000,1.4.1,85.0,20.0,gt4sd,,,,https://pypi.org/project/gt4sd,2024-06-13 13:19:51.000,,852.0,852.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, -86,ATOM3D,,datasets,MIT,https://github.com/drorlab/atom3d,ATOM3D: tasks on molecules in three dimensions.,18,False,"['biomolecules', 'benchmarking']",drorlab/atom3d,https://github.com/drorlab/atom3d,2020-04-03 22:53:11,2023-03-02 18:21:02.000,2023-03-02 18:20:29,798.0,,35.0,17.0,6.0,21.0,40.0,294.0,2022-07-20 00:58:03.115,0.2.6,15.0,10.0,atom3d,,40.0,40.0,https://pypi.org/project/atom3d,2022-07-20 00:58:03.115,,533.0,533.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -87,Neural Force Field,,ml-iap,MIT,https://github.com/learningmatter-mit/NeuralForceField,Neural Network Force Field based on PyTorch.,18,True,['pretrained'],learningmatter-mit/NeuralForceField,https://github.com/learningmatter-mit/NeuralForceField,2020-10-04 15:17:41,2024-08-11 22:33:54.000,2024-07-24 20:30:53,3121.0,74.0,47.0,7.0,5.0,1.0,18.0,225.0,2024-05-29 21:15:00.000,1.0.0,1.0,40.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -88,gpax,,math,MIT,https://github.com/ziatdinovmax/gpax,Gaussian Processes for Experimental Sciences.,18,True,"['probabilistic', 'active-learning']",ziatdinovmax/gpax,https://github.com/ziatdinovmax/gpax,2021-10-28 13:43:18,2024-08-09 21:37:33.000,2024-05-21 08:13:54,787.0,,22.0,7.0,68.0,8.0,32.0,195.0,2024-03-20 06:39:54.000,0.1.8,16.0,6.0,gpax,,1.0,1.0,https://pypi.org/project/gpax,2024-03-20 06:39:54.000,,168.0,168.0,,,,1.0,,,,,,,,,,,,1.0,,,,,,,,, -89,MatBench,,community,MIT,https://github.com/materialsproject/matbench,Matbench: Benchmarks for materials science property prediction.,18,True,"['datasets', 'benchmarking', 'model-repository']",materialsproject/matbench,https://github.com/materialsproject/matbench,2021-02-24 03:58:42,2024-06-26 23:31:08.000,2024-01-20 09:41:36,772.0,,42.0,8.0,297.0,39.0,26.0,105.0,2022-07-27 04:40:26.000,0.6,5.0,25.0,matbench,,18.0,16.0,https://pypi.org/project/matbench,2022-07-27 04:44:21.961,2.0,457.0,457.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, -90,kgcnn,,rep-learn,MIT,https://github.com/aimat-lab/gcnn_keras,"Graph convolutions in Keras with TensorFlow, PyTorch or Jax.",18,True,,aimat-lab/gcnn_keras,https://github.com/aimat-lab/gcnn_keras,2020-07-17 11:12:46,2024-05-06 10:08:41.000,2024-05-06 10:08:14,3099.0,,29.0,7.0,30.0,12.0,74.0,103.0,2024-02-27 12:33:28.000,4.0.1,28.0,7.0,kgcnn,,21.0,18.0,https://pypi.org/project/kgcnn,2024-02-27 12:33:28.000,3.0,270.0,270.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -91,apax,,ml-iap,MIT,https://github.com/apax-hub/apax,A flexible and performant framework for training machine learning potentials.,18,True,,apax-hub/apax,https://github.com/apax-hub/apax,2022-11-18 12:31:19,2024-08-12 22:21:46.000,2024-08-09 15:26:47,1705.0,245.0,1.0,4.0,209.0,14.0,101.0,14.0,2024-08-09 20:31:40.000,0.6.0,6.0,7.0,apax,,2.0,2.0,https://pypi.org/project/apax,2024-08-09 20:31:40.000,,307.0,307.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -92,DeepQMC,,ml-wft,MIT,https://github.com/deepqmc/deepqmc,Deep learning quantum Monte Carlo for electrons in real space.,17,True,,deepqmc/deepqmc,https://github.com/deepqmc/deepqmc,2019-12-06 14:50:59,2024-08-14 09:04:29.000,2024-08-14 09:04:27,1461.0,1.0,58.0,23.0,156.0,5.0,39.0,341.0,2023-11-20 10:38:40.000,1.1.2,11.0,13.0,deepqmc,,2.0,2.0,https://pypi.org/project/deepqmc,2023-11-20 10:38:40.000,,141.0,141.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, -93,escnn,,rep-learn,https://github.com/QUVA-Lab/escnn/blob/master/LICENSE,https://github.com/QUVA-Lab/escnn,Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/.,17,True,,QUVA-Lab/escnn,https://github.com/QUVA-Lab/escnn,2022-03-16 10:15:02,2024-06-26 14:25:20.000,2023-10-17 22:37:11,244.0,,42.0,18.0,33.0,38.0,36.0,339.0,2023-07-17 22:58:13.120,1.0.11,16.0,10.0,escnn,,6.0,,https://pypi.org/project/escnn,2022-04-01 11:46:00.000,6.0,504.0,504.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -94,M3GNet,,uip,BSD-3-Clause,https://github.com/materialsvirtuallab/m3gnet,Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art..,17,False,"['ml-iap', 'pretrained']",materialsvirtuallab/m3gnet,https://github.com/materialsvirtuallab/m3gnet,2022-01-18 18:10:58,2023-06-06 23:56:08.000,2023-06-06 23:56:03,261.0,,58.0,11.0,33.0,15.0,20.0,229.0,2022-11-17 23:25:35.000,0.2.4,16.0,15.0,m3gnet,,28.0,23.0,https://pypi.org/project/m3gnet,2022-11-17 23:25:34.805,5.0,626.0,626.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, -95,ChemNLP project,,language-models,MIT,https://github.com/OpenBioML/chemnlp,ChemNLP project.,17,True,['datasets'],OpenBioML/chemnlp,https://github.com/OpenBioML/chemnlp,2023-02-13 16:20:23,2024-08-15 21:04:45.000,2024-08-15 21:02:30,371.0,40.0,45.0,3.0,284.0,111.0,140.0,144.0,2023-08-07 12:49:57.000,2023.7.1,6.0,27.0,chemnlp,,1.0,,https://pypi.org/project/chemnlp,2023-08-07 12:49:57.000,1.0,72.0,72.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, -96,FitSNAP,,md,GPL-2.0,https://github.com/FitSNAP/FitSNAP,Software for generating SNAP machine-learning interatomic potentials.,17,True,,FitSNAP/FitSNAP,https://github.com/FitSNAP/FitSNAP,2019-09-12 14:46:18,2024-08-09 16:04:42.000,2024-08-09 16:04:41,1388.0,9.0,50.0,7.0,182.0,16.0,56.0,142.0,2023-06-28 16:00:48.000,3.1.0,7.0,24.0,,conda-forge/fitsnap3,2.0,2.0,,,,,169.0,https://anaconda.org/conda-forge/fitsnap3,2023-06-16 00:19:04.615,7640.0,2.0,,,,,,9.0,,,,,,,,,,,,,,, -97,matsciml,,rep-learn,MIT,https://github.com/IntelLabs/matsciml,Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery..,17,True,"['workflows', 'benchmarking']",IntelLabs/matsciml,https://github.com/IntelLabs/matsciml,2022-09-13 20:27:28,2024-08-16 01:07:03.000,2024-08-15 22:36:29,2374.0,225.0,17.0,5.0,216.0,20.0,35.0,135.0,2023-08-31 23:59:40.000,1.0.0,2.0,11.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -98,DADApy,,unsupervised,Apache-2.0,https://github.com/sissa-data-science/DADApy,Distance-based Analysis of DAta-manifolds in python.,17,True,,sissa-data-science/DADApy,https://github.com/sissa-data-science/DADApy,2021-02-16 17:45:23,2024-08-08 11:25:55.000,2024-07-24 22:45:27,825.0,36.0,17.0,7.0,110.0,9.0,27.0,101.0,2024-07-02 15:52:45.000,0.3.1,6.0,19.0,dadapy,,7.0,7.0,https://pypi.org/project/dadapy,2024-07-02 15:49:35.000,,131.0,131.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, -99,CatLearn,,rep-eng,GPL-3.0,https://github.com/SUNCAT-Center/CatLearn,,17,False,['surface-science'],SUNCAT-Center/CatLearn,https://github.com/SUNCAT-Center/CatLearn,2018-04-20 04:16:14,2024-06-28 07:53:45.000,2023-02-07 09:31:25,1960.0,,57.0,19.0,80.0,10.0,17.0,100.0,2020-03-27 09:27:26.000,0.6.2,27.0,22.0,catlearn,,6.0,5.0,https://pypi.org/project/catlearn,2020-03-27 09:27:26.000,1.0,170.0,170.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, -100,Open Databases Integration for Materials Design (OPTIMADE),,datasets,CC-BY-4.0,https://github.com/Materials-Consortia/OPTIMADE,Specification of a common REST API for access to materials databases.,17,True,,Materials-Consortia/OPTIMADE,https://github.com/Materials-Consortia/OPTIMADE,2018-01-08 23:32:29,2024-07-29 21:09:07.000,2024-06-12 09:31:09,1786.0,2.0,35.0,21.0,297.0,68.0,169.0,76.0,2024-06-10 16:32:29.000,1.2.0,9.0,21.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -101,Graphormer,,rep-learn,MIT,https://github.com/microsoft/Graphormer,Graphormer is a general-purpose deep learning backbone for molecular modeling.,16,True,"['transformer', 'pretrained']",microsoft/Graphormer,https://github.com/microsoft/Graphormer,2021-05-27 05:31:18,2024-06-07 17:01:35.000,2024-05-28 06:22:34,77.0,2.0,331.0,31.0,46.0,92.0,64.0,2045.0,2024-04-03 08:23:10.000,dig-v1.0,2.0,14.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -102,OpenML,,community,BSD-3,https://github.com/openml/OpenML,Open Machine Learning.,16,True,['datasets'],openml/OpenML,https://github.com/openml/OpenML,2012-12-11 11:27:40,2024-04-05 08:51:24.000,2024-01-12 08:40:06,2317.0,,90.0,48.0,202.0,367.0,560.0,656.0,,,,35.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -103,ChemCrow,,language-models,MIT,https://github.com/ur-whitelab/chemcrow-public,Open source package for the accurate solution of reasoning-intensive chemical tasks.,16,True,['ai-agent'],ur-whitelab/chemcrow-public,https://github.com/ur-whitelab/chemcrow-public,2023-06-04 15:59:05,2024-04-03 19:49:19.000,2024-03-27 04:32:41,110.0,,77.0,16.0,22.0,5.0,14.0,558.0,2024-03-27 04:30:13.000,0.3.24,27.0,3.0,chemcrow,,4.0,4.0,https://pypi.org/project/chemcrow,2024-03-27 04:30:13.000,,546.0,546.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -104,Uni-Fold,,biomolecules,Apache-2.0,https://github.com/dptech-corp/Uni-Fold,An open-source platform for developing protein models beyond AlphaFold.,16,True,,dptech-corp/Uni-Fold,https://github.com/dptech-corp/Uni-Fold,2022-07-30 03:37:29,2024-06-26 02:52:56.000,2024-01-08 06:19:47,98.0,,67.0,7.0,80.0,20.0,50.0,366.0,2022-10-19 12:44:31.000,2.2.0,3.0,7.0,,,,,,,,,161.0,,,,3.0,,,,,,3560.0,,,,,,,,,,,,,,, -105,GT4SD - Generative Toolkit for Scientific Discovery,,community,MIT,https://huggingface.co/GT4SD,Gradio apps of generative models in GT4SD.,16,True,"['generative', 'pretrained', 'drug-discovery', 'model-repository']",GT4SD/gt4sd-core,https://github.com/GT4SD/gt4sd-core,2022-02-11 19:06:58,2024-07-04 11:11:27.000,2024-07-04 11:11:26,296.0,8.0,66.0,17.0,148.0,14.0,98.0,327.0,2024-06-13 15:18:45.000,1.4.1,57.0,20.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -106,ChemDataExtractor,,language-models,MIT,https://github.com/mcs07/ChemDataExtractor,Automatically extract chemical information from scientific documents.,16,False,['literature-data'],mcs07/ChemDataExtractor,https://github.com/mcs07/ChemDataExtractor,2016-10-02 23:50:01,2023-07-27 18:05:13.000,2017-02-21 23:20:23,106.0,,106.0,18.0,16.0,21.0,9.0,296.0,2017-02-03 00:28:29.000,1.3.0,8.0,2.0,chemdataextractor,chemdataextractor/chemdataextractor,120.0,112.0,https://pypi.org/project/chemdataextractor,2017-02-03 00:12:36.000,8.0,696.0,760.0,https://anaconda.org/chemdataextractor/chemdataextractor,2023-06-16 13:17:47.249,3140.0,2.0,,,,,,2996.0,,,,,,,,,,,,,,, -107,QML,,general-tool,MIT,https://github.com/qmlcode/qml,QML: Quantum Machine Learning.,16,False,,qmlcode/qml,https://github.com/qmlcode/qml,2017-04-22 04:48:38,2024-04-12 13:38:21.000,2018-09-10 11:14:35,75.0,,81.0,23.0,101.0,30.0,19.0,197.0,2018-03-02 11:36:41.000,0.4.0,34.0,2.0,qml,,31.0,31.0,https://pypi.org/project/qml,2018-08-13 10:37:42.000,,423.0,423.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -108,openmm-torch,,md,https://github.com/openmm/openmm-torch#license,https://github.com/openmm/openmm-torch,OpenMM plugin to define forces with neural networks.,16,True,"['ml-iap', 'lang-cpp']",openmm/openmm-torch,https://github.com/openmm/openmm-torch,2019-09-27 18:15:19,2024-08-16 16:00:02.000,2024-08-16 16:00:01,72.0,5.0,24.0,12.0,61.0,25.0,65.0,177.0,2023-10-09 08:49:10.000,1.4,16.0,8.0,,conda-forge/openmm-torch,,,,,,,9376.0,https://anaconda.org/conda-forge/openmm-torch,2024-06-03 12:02:18.688,403182.0,2.0,,,,,,,,,,,,,,,,,,,,, -109,sGDML,,ml-iap,MIT,https://github.com/stefanch/sGDML,sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model.,16,True,,stefanch/sGDML,https://github.com/stefanch/sGDML,2018-07-11 15:20:30,2023-08-31 12:59:32.000,2023-08-31 12:57:53,205.0,,35.0,8.0,12.0,6.0,11.0,139.0,2023-08-31 12:58:49.000,1.0.2,21.0,8.0,sgdml,,10.0,9.0,https://pypi.org/project/sgdml,2023-08-31 12:59:32.000,1.0,200.0,200.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -110,XenonPy,,general-tool,BSD-3-Clause,https://github.com/yoshida-lab/XenonPy,XenonPy is a Python Software for Materials Informatics.,16,True,,yoshida-lab/XenonPy,https://github.com/yoshida-lab/XenonPy,2018-01-17 10:13:29,2024-07-15 21:14:48.000,2024-04-21 06:58:38,693.0,,57.0,11.0,184.0,21.0,66.0,131.0,2023-05-21 15:54:32.000,0.6.8,54.0,10.0,xenonpy,,1.0,,https://pypi.org/project/xenonpy,2022-10-31 15:40:18.355,1.0,265.0,283.0,,,,2.0,,,,,,1393.0,,,,,,,,,,,,,,, -111,MatBench Discovery,,community,MIT,https://github.com/janosh/matbench-discovery,An evaluation framework for machine learning models simulating high-throughput materials discovery.,16,True,"['datasets', 'benchmarking', 'model-repository']",janosh/matbench-discovery,https://github.com/janosh/matbench-discovery,2022-06-20 18:32:44,2024-08-18 09:46:39.000,2024-08-18 09:46:33,358.0,24.0,11.0,8.0,77.0,3.0,32.0,82.0,2024-07-15 20:08:07.000,1.2.0,8.0,7.0,matbench-discovery,,2.0,2.0,https://pypi.org/project/matbench-discovery,2024-07-15 20:08:07.000,,148.0,148.0,,,,2.0,,,,,,,,,,,,-1.0,,,,,,,,, -112,PMTransformer,,generative,MIT,https://github.com/hspark1212/MOFTransformer,"Universal Transfer Learning in Porous Materials, including MOFs.",16,True,"['transfer-learning', 'pretrained', 'transformer']",hspark1212/MOFTransformer,https://github.com/hspark1212/MOFTransformer,2021-12-11 06:30:12,2024-06-20 07:01:44.000,2024-06-20 06:57:57,410.0,6.0,11.0,5.0,127.0,,36.0,82.0,2024-06-20 07:02:24.000,2.2.0,17.0,2.0,moftransformer,,7.0,6.0,https://pypi.org/project/moftransformer,2024-06-20 07:01:44.000,1.0,320.0,320.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, -113,SpheriCart,,math,MIT,https://github.com/lab-cosmo/sphericart,Multi-language library for the calculation of spherical harmonics in Cartesian coordinates.,16,True,,lab-cosmo/sphericart,https://github.com/lab-cosmo/sphericart,2023-02-04 15:15:25,2024-08-19 09:25:24.000,2024-08-18 15:56:10,365.0,17.0,10.0,5.0,105.0,19.0,13.0,63.0,2023-04-27 09:57:29.640,0.3.0,10.0,10.0,sphericart,,3.0,3.0,https://pypi.org/project/sphericart,2024-01-23 17:06:36.000,,198.0,201.0,,,,2.0,,,,,,60.0,,,,,,,,,,,,,,, -114,mp-pyrho,,data-structures,https://github.com/materialsproject/pyrho,https://github.com/materialsproject/pyrho,Tools for re-griding volumetric quantum chemistry data for machine-learning purposes.,16,True,['ml-dft'],materialsproject/pyrho,https://github.com/materialsproject/pyrho,2020-05-25 22:44:02,2024-08-14 00:15:19.000,2024-02-23 02:53:46,287.0,,6.0,9.0,115.0,1.0,3.0,36.0,2024-02-23 02:55:26.000,0.4.4,28.0,8.0,mp-pyrho,,26.0,23.0,https://pypi.org/project/mp-pyrho,2024-02-23 02:55:26.000,3.0,5825.0,5825.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, -115,wfl,,ml-iap,GPL-2.0,https://github.com/libAtoms/workflow,Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows.,16,True,"['workflows', 'htc']",libAtoms/workflow,https://github.com/libAtoms/workflow,2021-11-04 17:03:34,2024-08-16 13:18:11.000,2024-08-16 12:23:47,1153.0,109.0,18.0,9.0,180.0,67.0,91.0,27.0,2024-04-25 15:07:11.000,0.2.4,4.0,18.0,,,1.0,1.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -116,IPSuite,,active-learning,EPL-2.0,https://github.com/zincware/IPSuite,A Python toolkit for FAIR development and deployment of machine-learned interatomic potentials.,16,True,"['ml-iap', 'md', 'workflows', 'htc', 'FAIR']",zincware/IPSuite,https://github.com/zincware/IPSuite,2023-03-01 16:34:45,2024-08-16 11:34:27.000,2024-08-16 11:34:25,443.0,37.0,8.0,3.0,203.0,67.0,65.0,18.0,2024-08-08 20:37:20.000,0.1.3,7.0,8.0,ipsuite,,6.0,6.0,https://pypi.org/project/ipsuite,2024-08-08 20:37:48.000,,142.0,142.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -117,MoLeR,,generative,MIT,https://github.com/microsoft/molecule-generation,Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation.,15,True,,microsoft/molecule-generation,https://github.com/microsoft/molecule-generation,2022-02-17 19:16:29,2024-01-05 14:31:05.000,2024-01-03 14:28:02,67.0,,38.0,11.0,37.0,10.0,28.0,250.0,2024-01-05 14:31:05.000,0.4.1,5.0,5.0,molecule-generation,,1.0,,https://pypi.org/project/molecule-generation,2024-01-05 14:31:05.000,1.0,174.0,174.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -118,Automatminer,,general-tool,https://github.com/hackingmaterials/automatminer/blob/main/LICENSE,https://github.com/hackingmaterials/automatminer,An automatic engine for predicting materials properties.,15,False,['automl'],hackingmaterials/automatminer,https://github.com/hackingmaterials/automatminer,2018-05-10 18:27:08,2023-11-12 10:09:39.000,2022-01-06 19:39:49,1666.0,,49.0,12.0,233.0,41.0,138.0,134.0,2020-07-28 02:19:07.000,1.0.3.20200727,17.0,13.0,automatminer,,8.0,8.0,https://pypi.org/project/automatminer,2020-07-28 02:23:45.000,,205.0,205.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, -119,MODNet,,rep-eng,MIT,https://github.com/ppdebreuck/modnet,MODNet: a framework for machine learning materials properties.,15,True,"['pretrained', 'small-data', 'transfer-learning']",ppdebreuck/modnet,https://github.com/ppdebreuck/modnet,2020-03-13 07:39:21,2024-06-24 13:31:56.000,2024-06-24 12:29:45,276.0,1.0,32.0,7.0,174.0,26.0,27.0,74.0,2024-05-07 14:09:13.000,0.4.4,21.0,8.0,,,7.0,7.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -120,Ultra-Fast Force Fields (UF3),,ml-iap,Apache-2.0,https://github.com/uf3/uf3,UF3: a python library for generating ultra-fast interatomic potentials.,15,True,,uf3/uf3,https://github.com/uf3/uf3,2021-10-01 13:21:44,2024-07-27 23:40:38.000,2024-07-12 22:56:23,703.0,21.0,20.0,6.0,82.0,19.0,30.0,57.0,2023-10-27 16:37:16.000,0.4.0,4.0,10.0,uf3,,1.0,1.0,https://pypi.org/project/uf3,2023-10-27 16:37:16.000,,44.0,44.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -121,aviary,,materials-discovery,MIT,https://github.com/CompRhys/aviary,The Wren sits on its Roost in the Aviary.,15,True,,CompRhys/aviary,https://github.com/CompRhys/aviary,2021-09-28 12:29:05,2024-08-05 20:10:13.000,2024-08-05 20:09:13,638.0,28.0,11.0,3.0,61.0,4.0,24.0,46.0,2024-07-22 19:03:03.000,1.0.0,5.0,4.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, -122,KLIFF,,ml-iap,LGPL-2.1,https://github.com/openkim/kliff,KIM-based Learning-Integrated Fitting Framework for interatomic potentials.,15,True,"['probabilistic', 'workflows']",openkim/kliff,https://github.com/openkim/kliff,2017-08-01 20:33:58,2024-08-14 00:02:36.000,2024-07-06 01:33:46,1073.0,8.0,20.0,4.0,148.0,22.0,19.0,34.0,2023-12-17 02:12:19.000,0.4.3,18.0,9.0,kliff,conda-forge/kliff,3.0,3.0,https://pypi.org/project/kliff,2023-12-17 02:12:19.000,,96.0,2178.0,https://anaconda.org/conda-forge/kliff,2023-12-18 18:25:23.770,95812.0,2.0,,,,,,,,,,,,,,,,,,,,, -123,Polynomials4ML.jl,,math,MIT,https://github.com/ACEsuit/Polynomials4ML.jl,"Polynomials for ML: fast evaluation, batching, differentiation.",15,True,['lang-julia'],ACEsuit/Polynomials4ML.jl,https://github.com/ACEsuit/Polynomials4ML.jl,2022-09-20 23:05:53,2024-08-18 21:49:29.000,2024-06-22 16:18:31,410.0,50.0,5.0,4.0,40.0,17.0,34.0,12.0,2024-06-22 16:34:35.000,0.3.1,17.0,10.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -124,benchmarking-gnns,,rep-learn,MIT,https://github.com/graphdeeplearning/benchmarking-gnns,Repository for benchmarking graph neural networks.,14,False,"['single-paper', 'benchmarking']",graphdeeplearning/benchmarking-gnns,https://github.com/graphdeeplearning/benchmarking-gnns,2020-03-03 03:42:50,2023-06-22 04:03:53.000,2022-05-10 13:22:20,45.0,,446.0,58.0,17.0,5.0,63.0,2478.0,,,,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -125,dlpack,,data-structures,Apache-2.0,https://github.com/dmlc/dlpack,common in-memory tensor structure.,14,True,['lang-cpp'],dmlc/dlpack,https://github.com/dmlc/dlpack,2017-02-24 16:56:47,2024-05-13 08:28:51.000,2024-03-26 12:57:17,75.0,,130.0,47.0,77.0,27.0,42.0,883.0,2023-01-05 18:42:00.000,0.8,9.0,23.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -126,FermiNet,,ml-wft,Apache-2.0,https://github.com/google-deepmind/ferminet,An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations.,14,True,['transformer'],google-deepmind/ferminet,https://github.com/google-deepmind/ferminet,2020-10-06 12:21:06,2024-06-04 15:46:46.000,2024-06-04 15:46:09,228.0,4.0,110.0,37.0,30.0,1.0,44.0,659.0,,,,18.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -127,SevenNet,,uip,GPL-3.0,https://github.com/MDIL-SNU/SevenNet,SevenNet (Scalable EquiVariance Enabled Neural Network) is a graph neural network interatomic potential package that..,14,True,"['ml-iap', 'md', 'pretrained']",MDIL-SNU/SevenNet,https://github.com/MDIL-SNU/SevenNet,2023-02-16 06:31:53,2024-08-19 07:35:26.000,2024-08-19 02:59:51,413.0,97.0,10.0,4.0,60.0,5.0,7.0,87.0,2024-07-26 01:40:27.000,0.9.3,6.0,8.0,,,1.0,1.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -128,PyXtalFF,,ml-iap,MIT,https://github.com/MaterSim/PyXtal_FF,Machine Learning Interatomic Potential Predictions.,14,True,,MaterSim/PyXtal_FF,https://github.com/MaterSim/PyXtal_FF,2019-01-08 08:43:35,2024-02-15 16:12:06.000,2024-01-07 14:27:45,561.0,,23.0,9.0,4.0,12.0,51.0,85.0,2023-06-09 17:17:24.000,0.2.3,19.0,9.0,pyxtal_ff,,,,https://pypi.org/project/pyxtal_ff,2022-12-21 20:21:00.409,,69.0,69.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -129,HydraGNN,,rep-learn,BSD-3,https://github.com/ORNL/HydraGNN,Distributed PyTorch implementation of multi-headed graph convolutional neural networks.,14,True,,ORNL/HydraGNN,https://github.com/ORNL/HydraGNN,2021-05-28 03:32:03,2024-08-19 01:37:13.000,2024-08-06 16:10:42,675.0,23.0,23.0,10.0,228.0,17.0,32.0,57.0,2023-11-10 15:25:43.000,3.0,2.0,13.0,,,1.0,1.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -130,SchNetPack G-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/schnetpack-gschnet,G-SchNet extension for SchNetPack.,14,True,,atomistic-machine-learning/schnetpack-gschnet,https://github.com/atomistic-machine-learning/schnetpack-gschnet,2022-04-21 12:34:13,2024-07-05 12:47:35.000,2024-07-05 12:44:53,164.0,31.0,8.0,4.0,1.0,,14.0,43.0,2024-07-03 16:43:48.000,1.1.0,3.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -131,load-atoms,,datasets,MIT,https://github.com/jla-gardner/load-atoms,download and manipulate atomistic datasets.,14,True,['data-structures'],jla-gardner/load-atoms,https://github.com/jla-gardner/load-atoms,2022-11-21 21:59:15,2024-04-06 05:14:57.000,2024-04-06 05:13:05,271.0,,2.0,1.0,31.0,2.0,29.0,37.0,2024-04-06 05:04:52.000,0.2.14,36.0,3.0,load-atoms,,3.0,3.0,https://pypi.org/project/load-atoms,2024-04-06 05:04:52.000,,256.0,256.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -132,SALTED,,ml-dft,GPL-3.0,https://github.com/andreagrisafi/SALTED,Symmetry-Adapted Learning of Three-dimensional Electron Densities.,14,True,,andreagrisafi/SALTED,https://github.com/andreagrisafi/SALTED,2020-01-22 10:24:29,2024-08-02 15:26:59.000,2024-07-08 10:41:32,699.0,49.0,4.0,3.0,39.0,1.0,5.0,30.0,2024-06-22 12:42:33.000,3.0.0,2.0,16.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, -133,SISSO,,rep-eng,Apache-2.0,https://github.com/rouyang2017/SISSO,A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models.,13,True,['lang-fortran'],rouyang2017/SISSO,https://github.com/rouyang2017/SISSO,2017-10-16 11:31:57,2024-06-19 15:58:39.000,2023-09-12 08:50:38,166.0,,76.0,6.0,3.0,18.0,57.0,229.0,,,,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -134,n2p2,,ml-iap,GPL-3.0,https://github.com/CompPhysVienna/n2p2,n2p2 - A Neural Network Potential Package.,13,False,['lang-cpp'],CompPhysVienna/n2p2,https://github.com/CompPhysVienna/n2p2,2018-07-25 12:29:17,2023-05-11 16:26:02.000,2022-09-05 10:56:20,387.0,,75.0,12.0,53.0,68.0,85.0,216.0,2022-05-23 12:53:39.000,2.2.0,11.0,9.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -135,Librascal,,rep-eng,LGPL-2.1,https://github.com/lab-cosmo/librascal,A scalable and versatile library to generate representations for atomic-scale learning.,13,True,,lab-cosmo/librascal,https://github.com/lab-cosmo/librascal,2018-02-01 08:38:51,2024-01-11 17:38:31.000,2023-11-30 14:48:28,2931.0,,20.0,22.0,201.0,115.0,132.0,80.0,,,3.0,30.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -136,NNPOps,,ml-iap,MIT,https://github.com/openmm/NNPOps,High-performance operations for neural network potentials.,13,True,"['md', 'lang-cpp']",openmm/NNPOps,https://github.com/openmm/NNPOps,2020-09-10 21:02:00,2024-07-10 15:29:02.000,2024-07-10 15:29:02,95.0,1.0,17.0,9.0,63.0,21.0,34.0,80.0,2023-07-26 11:21:58.000,0.6,7.0,9.0,,conda-forge/nnpops,,,,,,,6478.0,https://anaconda.org/conda-forge/nnpops,2024-05-31 15:49:32.883,187863.0,2.0,,,,,,,,,,,,,,,,,,,,, -137,OpenMM-ML,,md,MIT,https://github.com/openmm/openmm-ml,High level API for using machine learning models in OpenMM simulations.,13,True,['ml-iap'],openmm/openmm-ml,https://github.com/openmm/openmm-ml,2021-02-10 20:55:25,2024-08-06 19:01:36.000,2024-08-06 19:01:36,46.0,2.0,18.0,14.0,33.0,20.0,34.0,79.0,2024-06-06 16:49:09.000,1.2,6.0,5.0,,conda-forge/openmm-ml,,,,,,,193.0,https://anaconda.org/conda-forge/openmm-ml,2024-06-07 16:52:07.157,4641.0,3.0,,,,,,,,,,,,,,,,,,,,, -138,MLatom,,general-tool,MIT,https://github.com/dralgroup/mlatom,AI-enhanced computational chemistry.,13,True,"['uip', 'ml-iap', 'md', 'ml-dft', 'ml-esm', 'transfer-learning', 'active-learning', 'spectroscopy', 'structure-optimization']",dralgroup/mlatom,https://github.com/dralgroup/mlatom,2023-08-16 13:47:48,2024-07-25 06:05:25.000,2024-07-24 04:47:50,52.0,12.0,6.0,2.0,18.0,,1.0,36.0,2024-07-23 08:38:48.000,3.9.0,15.0,3.0,mlatom,,,,https://pypi.org/project/mlatom,2024-07-25 06:05:25.000,,872.0,872.0,,,,3.0,,,,,,,,,,,,1.0,,,,,,,,, -139,flare++,,active-learning,MIT,https://github.com/mir-group/flare_pp,A many-body extension of the FLARE code.,13,False,"['lang-cpp', 'ml-iap']",mir-group/flare_pp,https://github.com/mir-group/flare_pp,2019-11-20 22:46:32,2022-02-27 21:05:09.000,2022-02-24 19:00:50,989.0,,6.0,7.0,28.0,8.0,17.0,35.0,2021-12-23 05:02:12.000,0.1.1,25.0,10.0,flare_pp,,2.0,,https://pypi.org/project/flare_pp,2021-12-23 05:02:12.000,2.0,205.0,205.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -140,GlassPy,,rep-eng,GPL-3.0,https://github.com/drcassar/glasspy,Python module for scientists working with glass materials.,13,True,,drcassar/glasspy,https://github.com/drcassar/glasspy,2019-07-18 23:15:43,2024-08-11 17:13:25.000,2024-01-21 13:59:55,334.0,,7.0,6.0,13.0,1.0,5.0,26.0,2024-01-21 14:30:47.000,0.4.6,11.0,1.0,glasspy,,5.0,5.0,https://pypi.org/project/glasspy,2024-01-21 14:30:47.000,,150.0,150.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -141,Crystal Graph Convolutional Neural Networks (CGCNN),,rep-learn,MIT,https://github.com/txie-93/cgcnn,Crystal graph convolutional neural networks for predicting material properties.,12,False,,txie-93/cgcnn,https://github.com/txie-93/cgcnn,2018-03-14 20:41:21,2021-09-06 05:23:51.000,2021-09-06 05:23:38,25.0,,273.0,23.0,7.0,17.0,20.0,623.0,,,,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -142,mat2vec,,language-models,MIT,https://github.com/materialsintelligence/mat2vec,Supplementary Materials for Tshitoyan et al. Unsupervised word embeddings capture latent knowledge from materials..,12,False,['rep-learn'],materialsintelligence/mat2vec,https://github.com/materialsintelligence/mat2vec,2019-04-25 07:55:30,2023-05-06 22:45:49.000,2023-05-06 22:45:49,55.0,,174.0,40.0,7.0,6.0,18.0,616.0,,,,5.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -143,AI for Science Resources,,community,GPL-3.0 license,https://github.com/divelab/AIRS/blob/main/Overview/resources.md,"List of resources for AI4Science research, including learning resources.",12,True,,divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2024-07-12 20:06:37.000,2024-07-12 20:06:37,425.0,4.0,57.0,18.0,5.0,2.0,12.0,477.0,,,,28.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -144,QH9,,datasets,CC-BY-NC-SA-4.0,https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench/QH9,A Quantum Hamiltonian Prediction Benchmark.,12,True,['ml-dft'],divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2024-07-12 20:06:37.000,2024-07-12 20:06:37,425.0,4.0,57.0,18.0,5.0,2.0,12.0,477.0,,,,28.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -145,Artificial Intelligence for Science (AIRS),,general-tool,GPL-3.0 license,https://github.com/divelab/AIRS,Artificial Intelligence Research for Science (AIRS).,12,True,"['rep-learn', 'generative', 'ml-iap', 'md', 'ml-dft', 'ml-wft', 'biomolecules']",divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2024-07-12 20:06:37.000,2024-07-12 20:06:37,425.0,4.0,57.0,18.0,5.0,2.0,12.0,477.0,,,,28.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -146,QHNet,,ml-dft,GPL-3.0,https://github.com/divelab/AIRS/tree/main/OpenDFT/QHNet,Artificial Intelligence Research for Science (AIRS).,12,True,['rep-learn'],divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2024-07-12 20:06:37.000,2024-07-12 20:06:37,425.0,4.0,57.0,18.0,5.0,2.0,12.0,477.0,,,,28.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -147,Geometric GNN Dojo,,educational,MIT,https://github.com/chaitjo/geometric-gnn-dojo/blob/main/geometric_gnn_101.ipynb,"New to geometric GNNs: try our practical notebook, prepared for MPhil students at the University of Cambridge.",12,False,['rep-learn'],chaitjo/geometric-gnn-dojo,https://github.com/chaitjo/geometric-gnn-dojo,2023-01-21 20:08:45,2024-05-22 11:06:03.000,2023-06-18 23:17:32,26.0,,44.0,10.0,5.0,3.0,6.0,447.0,2023-06-18 23:20:44.000,0.2.0,2.0,3.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, -148,DeepLearningLifeSciences,,educational,MIT,https://github.com/deepchem/DeepLearningLifeSciences,Example code from the book Deep Learning for the Life Sciences.,12,False,,deepchem/DeepLearningLifeSciences,https://github.com/deepchem/DeepLearningLifeSciences,2019-02-05 17:16:18,2021-09-17 05:10:37.000,2021-09-17 05:10:37,52.0,,151.0,25.0,15.0,11.0,10.0,345.0,2019-10-28 18:46:28.000,1.0,1.0,10.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, -149,TensorMol,,ml-iap,GPL-3.0,https://github.com/jparkhill/TensorMol,Tensorflow + Molecules = TensorMol.,12,False,['single-paper'],jparkhill/TensorMol,https://github.com/jparkhill/TensorMol,2016-10-28 19:40:11,2021-02-11 00:12:00.000,2018-03-30 12:26:14,1724.0,,74.0,45.0,8.0,18.0,19.0,270.0,2017-11-08 18:05:50.000,0.1,1.0,12.0,,,1.0,1.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -150,gptchem,,language-models,MIT,https://github.com/kjappelbaum/gptchem,Use GPT-3 to solve chemistry problems.,12,True,,kjappelbaum/gptchem,https://github.com/kjappelbaum/gptchem,2023-01-06 15:34:32,2024-05-17 19:25:11.000,2023-10-04 11:27:09,147.0,,39.0,9.0,5.0,19.0,2.0,221.0,2023-11-30 09:31:51.000,0.0.4,4.0,4.0,gptchem,,,,https://pypi.org/project/gptchem,2023-10-04 11:28:07.000,,36.0,36.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -151,ANI-1,,ml-iap,MIT,https://github.com/isayev/ASE_ANI,ANI-1 neural net potential with python interface (ASE).,12,True,,isayev/ASE_ANI,https://github.com/isayev/ASE_ANI,2016-12-08 05:09:32,2024-03-11 21:50:26.000,2024-03-11 21:50:26,112.0,,55.0,33.0,9.0,16.0,21.0,220.0,,,,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -152,DMFF,,ml-iap,LGPL-3.0,https://github.com/deepmodeling/DMFF,DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable..,12,True,,deepmodeling/DMFF,https://github.com/deepmodeling/DMFF,2022-02-14 01:35:50,2024-08-16 15:18:26.000,2024-01-12 00:58:20,431.0,,40.0,9.0,159.0,10.0,16.0,146.0,2023-11-09 14:32:37.000,1.0.0,4.0,14.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -153,ASAP,,unsupervised,MIT,https://github.com/BingqingCheng/ASAP,ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.,12,True,,BingqingCheng/ASAP,https://github.com/BingqingCheng/ASAP,2019-08-11 12:45:14,2024-06-27 12:53:17.000,2024-06-27 12:53:00,763.0,3.0,28.0,7.0,37.0,6.0,19.0,142.0,2023-08-30 13:54:23.000,1,1.0,6.0,,,6.0,6.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -154,SPICE,,datasets,MIT,https://github.com/openmm/spice-dataset,A collection of QM data for training potential functions.,12,True,"['ml-iap', 'md']",openmm/spice-dataset,https://github.com/openmm/spice-dataset,2021-08-31 18:52:05,2024-08-16 23:44:23.000,2024-04-15 20:14:31,42.0,,8.0,17.0,47.0,15.0,45.0,141.0,2024-04-15 20:17:14.000,2.0.1,8.0,1.0,,,,,,,,,9.0,,,,2.0,,,,,,244.0,,,,,,,,,,,,,,, -155,So3krates (MLFF),,ml-iap,MIT,https://github.com/thorben-frank/mlff,Build neural networks for machine learning force fields with JAX.,12,True,,thorben-frank/mlff,https://github.com/thorben-frank/mlff,2022-09-30 07:40:17,2024-08-12 14:37:34.000,2024-08-06 09:40:56,138.0,7.0,13.0,7.0,21.0,3.0,6.0,69.0,2024-06-24 11:09:20.000,0.3.0,2.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -156,jarvis-tools-notebooks,,educational,NIST,https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks,A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/.,12,True,,JARVIS-Materials-Design/jarvis-tools-notebooks,https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks,2020-06-27 20:22:02,2024-08-14 02:50:36.000,2024-08-14 02:50:35,753.0,113.0,26.0,4.0,46.0,,,59.0,,,,5.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, -157,Neural fingerprint (nfp),,rep-learn,https://github.com/NREL/nfp/blob/master/LICENSE,https://github.com/NREL/nfp,Keras layers for end-to-end learning with rdkit and pymatgen.,12,False,,NREL/nfp,https://github.com/NREL/nfp,2018-11-20 23:55:23,2024-02-24 20:11:49.000,2022-06-14 22:18:28,143.0,,27.0,7.0,19.0,2.0,6.0,57.0,2022-04-27 17:05:25.000,0.3.12,13.0,4.0,,,13.0,13.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -158,ChatMOF,,language-models,MIT,https://github.com/Yeonghun1675/ChatMOF,Predict and Inverse design for metal-organic framework with large-language models (llms).,12,True,['generative'],Yeonghun1675/ChatMOF,https://github.com/Yeonghun1675/ChatMOF,2023-05-19 06:33:06,2024-07-01 05:01:35.000,2024-07-01 04:57:36,72.0,4.0,7.0,1.0,12.0,,5.0,53.0,2024-06-14 09:56:27.000,0.2.1,17.0,1.0,chatmof,,2.0,2.0,https://pypi.org/project/chatmof,2024-07-01 05:01:35.000,,285.0,285.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -159,Rascaline,,rep-eng,BSD-3-Clause,https://github.com/Luthaf/rascaline,Computing representations for atomistic machine learning.,12,True,"['lang-rust', 'lang-cpp']",Luthaf/rascaline,https://github.com/Luthaf/rascaline,2020-09-24 14:28:34,2024-08-14 13:15:55.000,2024-08-14 13:10:20,561.0,17.0,13.0,7.0,255.0,32.0,35.0,44.0,,,,14.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -160,synspace,,generative,MIT,https://github.com/whitead/synspace,Synthesis generative model.,12,False,,whitead/synspace,https://github.com/whitead/synspace,2022-12-28 00:59:14,2023-04-15 22:42:57.000,2023-04-15 18:04:16,27.0,,3.0,3.0,1.0,3.0,1.0,35.0,2023-04-15 22:48:00.713,0.3.0,3.0,2.0,synspace,,18.0,17.0,https://pypi.org/project/synspace,2023-01-16 17:29:00.461,1.0,903.0,903.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -161,BOSS,,materials-discovery,Apache-2.0,https://gitlab.com/cest-group/boss,Bayesian Optimization Structure Search (BOSS).,12,True,['probabilistic'],,,2020-02-12 08:48:33,2024-07-20 17:27:04.000,,,,10.0,,,2.0,28.0,20.0,2024-05-03 13:49:43.000,1.10.1,49.0,,aalto-boss,,,,https://pypi.org/project/aalto-boss,2024-07-20 17:27:04.000,,591.0,591.0,,,,1.0,,,,,,,,,,,,,,,,,,cest-group/boss,https://gitlab.com/cest-group/boss,, -162,Compositionally-Restricted Attention-Based Network (CrabNet),,rep-learn,MIT,https://github.com/sparks-baird/CrabNet,Predict materials properties using only the composition information!.,12,False,,sparks-baird/CrabNet,https://github.com/sparks-baird/CrabNet,2021-09-17 07:58:15,2023-06-19 09:35:52.000,2023-06-19 09:35:52,427.0,,4.0,1.0,54.0,15.0,2.0,12.0,2023-06-07 01:07:33.000,2.0.8,37.0,5.0,crabnet,,15.0,13.0,https://pypi.org/project/crabnet,2023-01-10 04:27:02.444,2.0,319.0,319.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -163,Deep Learning for Molecules and Materials Book,,educational,https://github.com/whitead/dmol-book/blob/main/LICENSE,https://dmol.pub/,Deep learning for molecules and materials book.,11,False,,whitead/dmol-book,https://github.com/whitead/dmol-book,2020-08-19 19:24:32,2023-07-02 18:02:57.000,2023-07-02 18:02:56,558.0,,111.0,16.0,92.0,28.0,129.0,604.0,,,,19.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -164,ReLeaSE,,reinforcement-learning,MIT,https://github.com/isayev/ReLeaSE,Deep Reinforcement Learning for de-novo Drug Design.,11,False,['drug-discovery'],isayev/ReLeaSE,https://github.com/isayev/ReLeaSE,2018-04-26 14:50:34,2021-12-08 19:49:36.000,2021-12-08 19:49:36,160.0,,130.0,19.0,9.0,27.0,8.0,347.0,,,,5.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, -165,DeepH-pack,,ml-dft,LGPL-3.0,https://github.com/mzjb/DeepH-pack,Deep neural networks for density functional theory Hamiltonian.,11,True,['lang-julia'],mzjb/DeepH-pack,https://github.com/mzjb/DeepH-pack,2022-05-13 02:51:32,2024-05-22 10:50:01.000,2024-05-22 10:50:01,66.0,2.0,36.0,7.0,17.0,12.0,38.0,201.0,2023-07-11 08:13:06.000,0.2.2,2.0,8.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -166,PiNN,,ml-iap,BSD-3-Clause,https://github.com/Teoroo-CMC/PiNN,A Python library for building atomic neural networks.,11,True,,Teoroo-CMC/PiNN,https://github.com/Teoroo-CMC/PiNN,2019-10-04 08:13:18,2024-08-02 06:55:47.000,2024-06-27 11:23:15,160.0,2.0,31.0,6.0,15.0,1.0,5.0,104.0,2019-10-09 09:21:30.000,0.3.0,1.0,5.0,,,,,,,,,4.0,,,,2.0,teoroo/pinn,https://hub.docker.com/r/teoroo/pinn,2024-06-27 11:32:07.538231,,243.0,,,,,,,,,,,,,,,, -167,tinker-hp,,ml-iap,https://github.com/TinkerTools/tinker-hp/blob/master/license-Tinker.pdf,https://github.com/TinkerTools/tinker-hp,Tinker-HP: High-Performance Massively Parallel Evolution of Tinker on CPUs & GPUs.,11,True,,TinkerTools/tinker-hp,https://github.com/TinkerTools/tinker-hp,2018-06-12 12:15:51,2024-08-05 15:47:54.000,2024-08-05 15:47:21,562.0,14.0,22.0,13.0,2.0,3.0,17.0,78.0,2019-11-24 16:21:50.000,published-version-V1,1.0,12.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -168,hippynn,,rep-learn,https://github.com/lanl/hippynn/blob/main/LICENSE.txt,https://github.com/lanl/hippynn,python library for atomistic machine learning.,11,True,['workflows'],lanl/hippynn,https://github.com/lanl/hippynn,2021-11-17 00:45:13,2024-08-14 20:00:55.000,2024-08-14 19:09:04,147.0,18.0,23.0,9.0,75.0,6.0,9.0,65.0,2024-01-29 22:04:53.000,hippynn-0.0.3,3.0,14.0,,,1.0,1.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -169,Neural-Network-Models-for-Chemistry,,community,,https://github.com/Eipgen/Neural-Network-Models-for-Chemistry,A collection of Nerual Network Models for chemistry.,11,True,['rep-learn'],Eipgen/Neural-Network-Models-for-Chemistry,https://github.com/Eipgen/Neural-Network-Models-for-Chemistry,2022-05-23 06:35:09,2024-08-13 07:05:05.000,2024-08-08 14:14:24,210.0,12.0,8.0,3.0,22.0,1.0,1.0,64.0,2024-07-17 02:01:45.000,0.0.5,5.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -170,Pacemaker,,ml-iap,https://github.com/ICAMS/python-ace/blob/master/LICENSE.md,https://cortner.github.io/ACEweb/software/,Python package for fitting atomic cluster expansion (ACE) potentials.,11,True,,ICAMS/python-ace,https://github.com/ICAMS/python-ace,2021-11-19 11:39:54,2024-07-22 19:16:32.000,2024-07-22 19:16:32,160.0,1.0,15.0,4.0,24.0,16.0,33.0,62.0,2022-10-24 21:50:17.233,0.2.8,2.0,5.0,python-ace,,,,https://pypi.org/project/python-ace,2022-10-24 21:50:17.233,,17.0,17.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -171,AMPtorch,,general-tool,GPL-3.0,https://github.com/ulissigroup/amptorch,AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch.,11,False,,ulissigroup/amptorch,https://github.com/ulissigroup/amptorch,2019-01-24 15:15:48,2023-07-16 02:11:38.000,2023-07-16 02:08:13,759.0,,32.0,10.0,99.0,7.0,26.0,59.0,2023-07-16 02:11:38.000,1.0,3.0,14.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -172,SIMPLE-NN,,ml-iap,GPL-3.0,https://github.com/MDIL-SNU/SIMPLE-NN,SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE version Neural Network).,11,False,,MDIL-SNU/SIMPLE-NN,https://github.com/MDIL-SNU/SIMPLE-NN,2018-03-26 23:53:35,2022-01-27 05:04:05.000,2022-01-27 05:04:05,586.0,,19.0,12.0,91.0,4.0,26.0,47.0,2021-09-23 01:41:42.000,1.1.1,9.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -173,DeepErwin,,ml-wft,https://github.com/mdsunivie/deeperwin/blob/master/LICENSE,https://github.com/mdsunivie/deeperwin,DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions..,11,True,,mdsunivie/deeperwin,https://github.com/mdsunivie/deeperwin,2021-06-14 15:18:32,2024-06-07 15:52:47.000,2024-06-07 15:52:33,66.0,6.0,6.0,3.0,5.0,,11.0,46.0,2024-03-25 13:47:47.000,transferable_atomic_orbitals,6.0,7.0,deeperwin,,,,https://pypi.org/project/deeperwin,2021-12-14 11:03:19.657,,61.0,61.0,,,,3.0,,,,,,8.0,,,,,,,,,,,,,,, -174,nlcc,,language-models,MIT,https://github.com/whitead/nlcc,Natural language computational chemistry command line interface.,11,False,['single-paper'],whitead/nlcc,https://github.com/whitead/nlcc,2021-08-19 18:23:52,2023-02-04 03:07:56.000,2023-02-04 03:06:33,144.0,,7.0,5.0,1.0,,9.0,44.0,2023-02-04 03:11:01.949,0.6.0,10.0,3.0,nlcc,,1.0,1.0,https://pypi.org/project/nlcc,2022-12-07 05:07:49.878,,69.0,69.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -175,pair_nequip,,md,MIT,https://github.com/mir-group/pair_nequip,LAMMPS pair style for NequIP.,11,True,"['ml-iap', 'rep-learn']",mir-group/pair_nequip,https://github.com/mir-group/pair_nequip,2021-04-02 15:28:02,2024-06-05 17:06:39.000,2024-06-05 17:06:39,101.0,4.0,12.0,9.0,8.0,9.0,20.0,41.0,2022-05-20 00:39:04.000,0.5.2,4.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -176,cmlkit,,rep-eng,MIT,https://github.com/sirmarcel/cmlkit,tools for machine learning in condensed matter physics and quantum chemistry.,11,False,['benchmarking'],sirmarcel/cmlkit,https://github.com/sirmarcel/cmlkit,2018-05-31 07:56:52,2022-04-01 00:39:14.000,2022-03-25 22:27:04,526.0,,6.0,3.0,1.0,6.0,2.0,34.0,,,25.0,1.0,cmlkit,,6.0,5.0,https://pypi.org/project/cmlkit,2022-03-25 22:27:16.000,1.0,176.0,176.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -177,NeuralXC,,ml-dft,BSD-3-Clause,https://github.com/semodi/neuralxc,Implementation of a machine learned density functional.,11,False,,semodi/neuralxc,https://github.com/semodi/neuralxc,2019-03-14 18:13:40,2024-06-17 22:55:40.000,2021-07-05 21:36:23,337.0,,10.0,5.0,10.0,5.0,5.0,33.0,,,3.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -178,CBFV,,rep-eng,,https://github.com/Kaaiian/CBFV,Tool to quickly create a composition-based feature vector.,11,False,,kaaiian/CBFV,https://github.com/Kaaiian/CBFV,2019-09-05 23:07:46,2022-03-30 05:47:53.000,2021-10-24 17:10:17,49.0,,6.0,4.0,7.0,5.0,5.0,23.0,2021-10-24 17:22:06.000,1.1.0,3.0,3.0,CBFV,,9.0,9.0,https://pypi.org/project/CBFV,2021-10-24 17:22:06.000,,245.0,245.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -179,BenchML,,rep-eng,Apache-2.0,https://github.com/capoe/benchml,ML benchmarking and pipeling framework.,11,False,['benchmarking'],capoe/benchml,https://github.com/capoe/benchml,2020-04-28 13:26:29,2023-05-24 15:13:06.000,2023-05-24 15:04:57,341.0,,4.0,5.0,7.0,3.0,10.0,15.0,2022-07-14 08:49:29.365,0.3.4,3.0,9.0,benchml,,,,https://pypi.org/project/benchml,2022-07-14 08:49:29.365,,96.0,96.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -180,calorine,,ml-iap,https://gitlab.com/materials-modeling/calorine/-/blob/master/LICENSE,https://calorine.materialsmodeling.org/,A Python package for constructing and sampling neuroevolution potential models. https://doi.org/10.21105/joss.06264.,11,True,,,,2021-04-23 16:12:56,2024-07-26 09:35:09.000,,,,4.0,,,9.0,76.0,12.0,2024-02-28 15:53:18.000,2.2.1,14.0,,calorine,,2.0,,https://pypi.org/project/calorine,2024-07-26 09:35:09.000,2.0,1231.0,1231.0,,,,2.0,,,,,,,,,,,,,,,,,,materials-modeling/calorine,https://gitlab.com/materials-modeling/calorine,, -181,CCS_fit,,ml-iap,GPL-3.0,https://github.com/Teoroo-CMC/CCS,Curvature Constrained Splines.,11,True,,Teoroo-CMC/CCS,https://github.com/Teoroo-CMC/CCS,2021-12-13 14:29:53,2024-05-14 08:53:09.000,2024-02-16 09:31:25,762.0,,11.0,3.0,13.0,8.0,6.0,8.0,2024-02-16 09:31:34.000,0.22.5,100.0,8.0,ccs_fit,,,,https://pypi.org/project/ccs_fit,2024-02-16 09:31:34.000,,408.0,428.0,,,,2.0,,,,,,424.0,,,,,,,,,,,,,,, -182,pretrained-gnns,,rep-learn,MIT,https://github.com/snap-stanford/pretrain-gnns,Strategies for Pre-training Graph Neural Networks.,10,False,['pretrained'],snap-stanford/pretrain-gnns,https://github.com/snap-stanford/pretrain-gnns,2020-01-30 22:12:41,2023-07-29 06:21:39.000,2023-07-29 06:21:39,13.0,,160.0,17.0,8.0,34.0,29.0,954.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -183,GNoME Explorer,,community,Apache-2.0,https://next-gen.materialsproject.org/materials/gnome,Graph Networks for Materials Exploration Database.,10,True,"['datasets', 'materials-discovery']",google-deepmind/materials_discovery,https://github.com/google-deepmind/materials_discovery,2023-11-28 10:29:51,2024-07-09 19:00:36.000,2023-12-02 03:54:29,8.0,,133.0,47.0,7.0,18.0,4.0,858.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -184,Materials Discovery: GNoME,,materials-discovery,Apache-2.0,https://github.com/google-deepmind/materials_discovery,"Graph Networks for Materials Science (GNoME) and dataset of 381,000 novel stable materials.",10,True,"['uip', 'datasets', 'rep-learn', 'proprietary']",google-deepmind/materials_discovery,https://github.com/google-deepmind/materials_discovery,2023-11-28 10:29:51,2024-07-09 19:00:36.000,2023-12-02 03:54:29,8.0,,133.0,47.0,7.0,18.0,4.0,858.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -185,OpenChem,,general-tool,MIT,https://github.com/Mariewelt/OpenChem,OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research.,10,False,,Mariewelt/OpenChem,https://github.com/Mariewelt/OpenChem,2018-07-10 01:27:33,2023-11-26 05:03:36.000,2022-04-27 19:27:40,444.0,,111.0,36.0,12.0,15.0,2.0,667.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -186,Awesome Materials Informatics,,community,https://github.com/tilde-lab/awesome-materials-informatics#license,https://github.com/tilde-lab/awesome-materials-informatics,"Curated list of known efforts in materials informatics, i.e. in modern materials science.",10,True,,tilde-lab/awesome-materials-informatics,https://github.com/tilde-lab/awesome-materials-informatics,2018-02-15 15:14:16,2024-07-12 09:19:42.000,2024-07-12 09:19:42,138.0,3.0,80.0,17.0,54.0,,8.0,359.0,2023-03-02 19:56:59.000,2023.03.02,1.0,19.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -187,Allegro,,ml-iap,MIT,https://github.com/mir-group/allegro,Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic..,10,False,,mir-group/allegro,https://github.com/mir-group/allegro,2022-02-06 23:50:40,2024-07-01 20:43:10.000,2023-05-08 21:16:45,38.0,,44.0,20.0,5.0,18.0,15.0,310.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -188,GDC,,rep-learn,MIT,https://github.com/gasteigerjo/gdc,"Graph Diffusion Convolution, as proposed in Diffusion Improves Graph Learning (NeurIPS 2019).",10,False,['generative'],gasteigerjo/gdc,https://github.com/gasteigerjo/gdc,2019-10-26 16:05:11,2023-04-26 14:22:40.000,2023-04-26 14:22:40,28.0,,42.0,3.0,1.0,1.0,11.0,262.0,,,,3.0,,,1.0,1.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -189,AI4Chemistry course,,educational,MIT,https://github.com/schwallergroup/ai4chem_course,"EPFL AI for chemistry course, Spring 2023. https://schwallergroup.github.io/ai4chem_course.",10,True,['chemistry'],schwallergroup/ai4chem_course,https://github.com/schwallergroup/ai4chem_course,2022-08-22 07:29:30,2024-05-02 20:41:12.000,2024-05-02 20:41:12,232.0,,30.0,4.0,9.0,1.0,3.0,127.0,,,,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -190,DeePKS-kit,,ml-dft,LGPL-3.0,https://github.com/deepmodeling/deepks-kit,a package for developing machine learning-based chemically accurate energy and density functional models.,10,True,,deepmodeling/deepks-kit,https://github.com/deepmodeling/deepks-kit,2020-07-29 03:27:50,2024-08-08 16:28:48.000,2024-04-13 03:44:40,384.0,,35.0,14.0,44.0,5.0,14.0,96.0,,,,7.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -191,Grad DFT,,ml-dft,Apache-2.0,https://github.com/XanaduAI/GradDFT,GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation..,10,True,,XanaduAI/GradDFT,https://github.com/XanaduAI/GradDFT,2023-05-15 16:18:25,2024-02-13 16:05:53.000,2024-02-13 16:05:51,419.0,,5.0,5.0,43.0,11.0,43.0,71.0,,,,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -192,LLaMP,,language-models,BSD-3-Clause,https://github.com/chiang-yuan/llamp,A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials informatics. An..,10,True,"['materials-discovery', 'cheminformatics', 'generative', 'MD', 'multimodal', 'language-models', 'lang-py', 'general-tool']",chiang-yuan/llamp,https://github.com/chiang-yuan/llamp,2023-07-01 08:15:34,2024-08-11 08:26:47.000,2024-08-01 23:06:38,372.0,7.0,7.0,,30.0,8.0,17.0,51.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -193,DSECOP,,educational,CCO-1.0,https://github.com/GDS-Education-Community-of-Practice/DSECOP,This repository contains data science educational materials developed by DSECOP Fellows.,10,True,,GDS-Education-Community-of-Practice/DSECOP,https://github.com/GDS-Education-Community-of-Practice/DSECOP,2022-03-07 17:47:33,2024-06-26 14:49:22.000,2024-06-26 14:49:19,555.0,6.0,25.0,10.0,25.0,1.0,7.0,43.0,,,,13.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -194,Finetuna,,active-learning,MIT,https://github.com/ulissigroup/finetuna,Active Learning for Machine Learning Potentials.,10,True,,ulissigroup/finetuna,https://github.com/ulissigroup/finetuna,2020-09-22 14:39:52,2024-05-15 17:26:24.000,2024-05-15 17:25:23,1200.0,,11.0,3.0,40.0,5.0,15.0,42.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -195,Atom2Vec,,rep-learn,MIT,https://github.com/idocx/Atom2Vec,Atom2Vec: a simple way to describe atoms for machine learning.,10,True,,idocx/Atom2Vec,https://github.com/idocx/Atom2Vec,2020-01-18 23:31:47,2024-02-23 21:44:03.000,2024-02-23 21:43:58,4.0,,9.0,1.0,1.0,2.0,1.0,32.0,2024-02-23 21:43:41.000,1.1.0,2.0,1.0,atom2vec,,2.0,2.0,https://pypi.org/project/atom2vec,2024-02-23 21:43:41.000,,69.0,69.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -196,OpenKIM,,datasets,LGPL-2.1,https://openkim.org/,"The Open Knowledgebase of Interatomic Models (OpenKIM) aims to be an online resource for standardized testing, long-..",10,False,"['model-repository', 'knowledge-base', 'pretrained']",openkim/kim-api,https://github.com/openkim/kim-api,2014-07-28 21:21:08,2023-08-16 00:09:44.000,2022-03-17 23:01:36,2371.0,,20.0,12.0,55.0,17.0,18.0,31.0,,,,24.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -197,FAENet,,rep-learn,MIT,https://github.com/vict0rsch/faenet,Frame Averaging Equivariant GNN for materials modeling.,10,True,,vict0rsch/faenet,https://github.com/vict0rsch/faenet,2023-02-10 22:10:27,2023-10-12 08:46:26.000,2023-10-12 08:46:22,125.0,,2.0,3.0,5.0,,,25.0,2023-09-12 04:00:49.000,0.1.2,3.0,3.0,faenet,,2.0,2.0,https://pypi.org/project/faenet,2023-09-14 21:06:36.000,,77.0,77.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -198,Point Edge Transformer (PET),,ml-iap,MIT,https://github.com/spozdn/pet,Point Edge Transformer.,10,True,"['rep-learn', 'transformer']",spozdn/pet,https://github.com/spozdn/pet,2023-02-08 18:36:10,2024-07-18 13:35:00.000,2024-07-02 10:29:58,201.0,10.0,5.0,4.0,12.0,5.0,,18.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -199,ACEhamiltonians,,ml-dft,MIT,https://github.com/ACEsuit/ACEhamiltonians.jl,"Provides tools for constructing, fitting, and predicting self-consistent Hamiltonian and overlap matrices in solid-..",10,False,['lang-julia'],ACEsuit/ACEhamiltonians.jl,https://github.com/ACEsuit/ACEhamiltonians.jl,2022-01-17 20:54:22,2024-06-05 15:25:30.000,2023-04-12 15:04:14,33.0,,6.0,5.0,42.0,2.0,3.0,12.0,2024-02-07 16:35:47.000,0.1.0,2.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -200,SiMGen,,generative,MIT,https://github.com/RokasEl/simgen,Zero Shot Molecular Generation via Similarity Kernels.,10,True,['visualization'],RokasEl/simgen,https://github.com/RokasEl/simgen,2023-01-25 16:41:18,2024-06-13 15:43:18.000,2024-02-15 10:31:41,257.0,,,2.0,23.0,1.0,3.0,11.0,2024-02-14 10:35:02.000,0.1.0,2.0,4.0,simgen,,1.0,1.0,https://pypi.org/project/simgen,2024-02-14 11:08:25.000,,34.0,34.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, -201,NNsforMD,,ml-iap,MIT,https://github.com/aimat-lab/NNsForMD,"Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings.",10,False,,aimat-lab/NNsForMD,https://github.com/aimat-lab/NNsForMD,2020-08-31 11:14:18,2022-11-10 13:04:49.000,2022-11-10 13:04:45,265.0,,6.0,3.0,,,,10.0,2022-04-12 15:15:00.183,2.0.0,5.0,2.0,pyNNsMD,,1.0,1.0,https://pypi.org/project/pyNNsMD,2022-04-12 15:15:00.183,,50.0,50.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, -202,ACEfit,,ml-iap,MIT,https://github.com/ACEsuit/ACEfit.jl,,10,True,['lang-julia'],ACEsuit/ACEfit.jl,https://github.com/ACEsuit/ACEfit.jl,2022-01-01 00:09:17,2024-08-13 04:26:08.000,2024-08-13 04:22:01,226.0,5.0,5.0,4.0,22.0,22.0,33.0,7.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -203,SE(3)-Transformers,,rep-learn,MIT,https://github.com/FabianFuchsML/se3-transformer-public,code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503.,9,False,"['single-paper', 'transformer']",FabianFuchsML/se3-transformer-public,https://github.com/FabianFuchsML/se3-transformer-public,2020-08-31 10:36:57,2023-07-10 05:13:25.000,2021-11-18 09:11:56,63.0,,67.0,17.0,5.0,11.0,17.0,482.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -204,EDM,,generative,MIT,https://github.com/ehoogeboom/e3_diffusion_for_molecules,E(3) Equivariant Diffusion Model for Molecule Generation in 3D.,9,False,,ehoogeboom/e3_diffusion_for_molecules,https://github.com/ehoogeboom/e3_diffusion_for_molecules,2022-04-15 14:34:35,2022-07-10 17:56:18.000,2022-07-10 17:56:12,6.0,,109.0,8.0,,10.0,36.0,417.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -205,DimeNet,,ml-iap,https://github.com/gasteigerjo/dimenet/blob/master/LICENSE.md,https://github.com/gasteigerjo/dimenet,"DimeNet and DimeNet++ models, as proposed in Directional Message Passing for Molecular Graphs (ICLR 2020) and Fast and..",9,True,,gasteigerjo/dimenet,https://github.com/gasteigerjo/dimenet,2020-02-14 12:40:15,2023-10-03 09:57:19.000,2023-10-03 09:57:19,103.0,,59.0,4.0,,1.0,30.0,280.0,,,,2.0,,,1.0,1.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -206,MoLFormers UI,,community,Apache-2.0,https://molformer.res.ibm.com/,A family of foundation models trained on chemicals.,9,True,"['transformer', 'language-models', 'pretrained', 'drug-discovery']",IBM/molformer,https://github.com/IBM/molformer,2022-11-07 18:48:17,2023-10-16 16:34:25.000,2023-10-16 16:33:13,7.0,,40.0,10.0,3.0,9.0,10.0,231.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -207,MoLFormer,,language-models,Apache-2.0,https://github.com/IBM/molformer,Repository for MolFormer.,9,True,"['transformer', 'pretrained', 'drug-discovery']",IBM/molformer,https://github.com/IBM/molformer,2022-11-07 18:48:17,2023-10-16 16:34:25.000,2023-10-16 16:33:13,7.0,,40.0,10.0,3.0,9.0,10.0,231.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -208,SchNet,,ml-iap,MIT,https://github.com/atomistic-machine-learning/SchNet,SchNet - a deep learning architecture for quantum chemistry.,9,False,,atomistic-machine-learning/SchNet,https://github.com/atomistic-machine-learning/SchNet,2017-10-03 11:52:20,2018-09-04 08:42:35.000,2018-09-04 08:42:34,53.0,,65.0,16.0,,1.0,2.0,212.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -209,Equiformer,,rep-learn,MIT,https://github.com/atomicarchitects/equiformer,[ICLR23 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs.,9,True,['transformer'],atomicarchitects/equiformer,https://github.com/atomicarchitects/equiformer,2023-02-28 00:21:30,2024-07-27 08:42:53.000,2024-07-18 10:32:17,6.0,3.0,36.0,5.0,2.0,5.0,8.0,187.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -210,GemNet,,ml-iap,https://github.com/TUM-DAML/gemnet_pytorch/blob/master/LICENSE,https://github.com/TUM-DAML/gemnet_pytorch,"GemNet model in PyTorch, as proposed in GemNet: Universal Directional Graph Neural Networks for Molecules (NeurIPS..",9,False,,TUM-DAML/gemnet_pytorch,https://github.com/TUM-DAML/gemnet_pytorch,2021-10-11 07:30:36,2023-04-26 14:20:12.000,2023-04-26 14:20:12,36.0,,27.0,4.0,1.0,,14.0,176.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -211,MolSkill,,language-models,MIT,https://github.com/microsoft/molskill,Extracting medicinal chemistry intuition via preference machine learning.,9,True,"['drug-discovery', 'recommender']",microsoft/molskill,https://github.com/microsoft/molskill,2023-01-12 13:48:31,2023-10-31 17:03:36.000,2023-10-31 17:03:36,81.0,,9.0,6.0,8.0,2.0,4.0,101.0,2023-08-04 12:22:15.000,1.2b,5.0,4.0,,msr-ai4science/molskill,,,,,,,15.0,https://anaconda.org/msr-ai4science/molskill,2023-06-18 17:27:43.196,275.0,3.0,,,,,,,,,,,,,,,,,,,,, -212,GATGNN: Global Attention Graph Neural Network,,rep-learn,MIT,https://github.com/superlouis/GATGNN,Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials..,9,False,,superlouis/GATGNN,https://github.com/superlouis/GATGNN,2020-06-21 03:27:36,2022-10-03 21:57:33.000,2022-10-03 21:57:33,99.0,,17.0,8.0,,3.0,3.0,67.0,2021-04-05 06:49:29.000,0.2,2.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -213,ACE.jl,,ml-iap,https://github.com/ACEsuit/ACE.jl/blob/main/license/mit.md,https://github.com/ACEsuit/ACE.jl,Parameterisation of Equivariant Properties of Particle Systems.,9,False,['lang-julia'],ACEsuit/ACE.jl,https://github.com/ACEsuit/ACE.jl,2019-11-30 16:22:51,2023-06-09 21:31:30.000,2023-06-09 21:29:10,912.0,,15.0,8.0,65.0,24.0,58.0,65.0,,,,12.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -214,PROPhet,,ml-dft,GPL-3.0,https://github.com/biklooost/PROPhet,PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches.,9,False,"['ml-iap', 'md', 'single-paper', 'lang-cpp']",biklooost/PROPhet,https://github.com/biklooost/PROPhet,2016-09-16 16:21:06,2018-04-19 02:09:46.000,2018-04-19 02:00:46,120.0,,26.0,14.0,6.0,9.0,7.0,62.0,2018-04-15 16:55:15.000,1.2,3.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -215,AI for Science paper collection,,community,Apache-2.0,https://github.com/sherrylixuecheng/AI_for_Science_paper_collection,List the AI for Science papers accepted by top conferences.,9,True,,sherrylixuecheng/AI_for_Science_paper_collection,https://github.com/sherrylixuecheng/AI_for_Science_paper_collection,2024-06-28 16:20:57,2024-08-18 19:18:00.000,2024-08-18 18:03:30,72.0,72.0,5.0,2.0,9.0,,,44.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -216,GAP,,ml-iap,https://github.com/libAtoms/GAP/blob/main/LICENSE.md,https://libatoms.github.io/,Gaussian Approximation Potential (GAP).,9,True,,libAtoms/GAP,https://github.com/libAtoms/GAP,2021-03-22 14:48:56,2024-08-17 08:35:27.000,2024-08-17 08:35:27,206.0,3.0,20.0,10.0,67.0,,,39.0,,,,13.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -217,pair_allegro,,md,MIT,https://github.com/mir-group/pair_allegro,LAMMPS pair style for Allegro deep learning interatomic potentials with parallelization support.,9,True,"['ml-iap', 'rep-learn']",mir-group/pair_allegro,https://github.com/mir-group/pair_allegro,2021-08-09 17:26:51,2024-06-05 17:00:50.000,2024-06-05 17:00:50,101.0,10.0,8.0,10.0,3.0,10.0,18.0,34.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -218,ALF,,ml-iap,https://github.com/lanl/ALF/blob/main/LICENSE,https://github.com/lanl/ALF,A framework for performing active learning for training machine-learned interatomic potentials.,9,True,['active-learning'],lanl/alf,https://github.com/lanl/ALF,2023-01-04 23:13:24,2024-08-08 16:59:38.000,2024-08-08 16:59:10,149.0,3.0,11.0,8.0,27.0,,,29.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -219,lie-nn,,math,MIT,https://github.com/lie-nn/lie-nn,Tools for building equivariant polynomials on reductive Lie groups.,9,False,['rep-learn'],lie-nn/lie-nn,https://github.com/lie-nn/lie-nn,2022-04-01 18:02:49,2023-06-29 19:38:34.000,2023-06-20 22:30:53,249.0,,1.0,8.0,3.0,1.0,,26.0,2023-06-20 22:31:12.000,0.0.0,1.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -220,iam-notebooks,,educational,Apache-2.0,https://github.com/ceriottm/iam-notebooks,Jupyter notebooks for the lectures of the Introduction to Atomistic Modeling.,9,True,,ceriottm/iam-notebooks,https://github.com/ceriottm/iam-notebooks,2020-11-23 21:27:41,2024-06-26 12:42:53.000,2024-06-26 12:42:45,242.0,2.0,5.0,4.0,7.0,4.0,,24.0,,,,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -221,PACE,,md,https://github.com/ICAMS/lammps-user-pace/blob/main/LICENSE,https://github.com/ICAMS/lammps-user-pace,"The LAMMPS ML-IAP `pair_style pace`, aka Atomic Cluster Expansion (ACE), aka ML-PACE,..",9,True,,ICAMS/lammps-user-pace,https://github.com/ICAMS/lammps-user-pace,2021-02-25 10:04:48,2024-07-10 09:17:44.000,2023-11-27 21:28:13,59.0,,10.0,6.0,16.0,2.0,6.0,23.0,2023-11-25 21:58:41.000,.2023.11.25.fix,6.0,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -222,SkipAtom,,rep-eng,MIT,https://github.com/lantunes/skipatom,"Distributed representations of atoms, inspired by the Skip-gram model.",9,False,,lantunes/skipatom,https://github.com/lantunes/skipatom,2021-06-19 13:09:13,2023-07-16 19:28:39.000,2022-05-04 13:18:30,46.0,,3.0,2.0,7.0,3.0,1.0,23.0,2022-05-04 13:20:18.000,1.2.5,12.0,1.0,skipatom,conda-forge/skipatom,1.0,1.0,https://pypi.org/project/skipatom,2022-05-04 13:20:18.000,,143.0,203.0,https://anaconda.org/conda-forge/skipatom,2023-06-18 08:42:05.505,1502.0,3.0,,,,,,,,,,,,,,,,,,,,, -223,UVVisML,,rep-learn,MIT,https://github.com/learningmatter-mit/uvvisml,Predict optical properties of molecules with machine learning.,9,False,"['optical-properties', 'single-paper', 'probabilistic']",learningmatter-mit/uvvisml,https://github.com/learningmatter-mit/uvvisml,2021-10-13 05:58:48,2023-05-26 22:35:14.000,2023-05-26 22:35:14,17.0,,6.0,4.0,1.0,,,21.0,2022-02-06 18:14:14.000,0.0.2,2.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -224,TurboGAP,,ml-iap,https://github.com/mcaroba/turbogap/blob/master/LICENSE.md,https://github.com/mcaroba/turbogap,The TurboGAP code.,9,True,['lang-fortran'],mcaroba/turbogap,https://github.com/mcaroba/turbogap,2021-05-02 09:19:05,2024-08-08 08:26:12.000,2024-07-09 06:58:53,309.0,12.0,8.0,8.0,7.0,6.0,3.0,16.0,,,,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -225,OPTIMADE Tutorial Exercises,,educational,MIT,https://github.com/Materials-Consortia/optimade-tutorial-exercises,Tutorial exercises for the OPTIMADE API.,9,True,['datasets'],Materials-Consortia/optimade-tutorial-exercises,https://github.com/Materials-Consortia/optimade-tutorial-exercises,2021-08-25 17:33:15,2023-09-27 08:32:31.000,2023-09-27 08:32:30,49.0,,7.0,12.0,15.0,,3.0,14.0,2023-06-12 07:47:14.000,2.0.1,5.0,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -226,Q-stack,,ml-dft,MIT,https://github.com/lcmd-epfl/Q-stack,Stack of codes for dedicated pre- and post-processing tasks for Quantum Machine Learning (QML).,9,True,"['excited-states', 'general-tool']",lcmd-epfl/Q-stack,https://github.com/lcmd-epfl/Q-stack,2021-10-20 15:33:26,2024-07-25 14:47:23.000,2024-07-19 11:29:08,413.0,32.0,5.0,2.0,45.0,9.0,20.0,14.0,,,1.0,7.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -227,AGOX,,materials-discovery,GPL-3.0,https://agox.gitlab.io/agox/,AGOX is a package for global optimization of atomic system using e.g. the energy calculated from density functional..,9,True,['structure-optimization'],,,2022-03-08 09:08:13,2024-08-03 18:26:35.000,,,,5.0,,,14.0,9.0,13.0,2023-08-31 12:33:38.000,3.1.1,4.0,,agox,,,,https://pypi.org/project/agox,2024-08-03 18:26:35.000,,211.0,211.0,,,,2.0,,,,,,,,,,,,,,,,,,agox/agox,https://gitlab.com/agox/agox,, -228,Materials Data Facility (MDF),,datasets,Apache-2.0,https://www.materialsdatafacility.org,"A simple way to publish, discover, and access materials datasets. Publication of very large datasets supported (e.g.,..",9,True,,materials-data-facility/connect_client,https://github.com/materials-data-facility/connect_client,2018-09-12 20:49:58,2024-03-10 03:11:45.000,2024-02-05 22:48:40,158.0,,1.0,4.0,35.0,1.0,6.0,10.0,2024-02-05 22:49:58.000,0.5.0,23.0,7.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -229,optimade.science,,community,MIT,https://optimade.science,A sky-scanner Optimade browser-only GUI.,9,True,['datasets'],tilde-lab/optimade.science,https://github.com/tilde-lab/optimade.science,2019-06-08 14:10:54,2024-06-10 12:03:39.000,2024-06-10 12:03:39,247.0,4.0,2.0,4.0,32.0,7.0,19.0,8.0,2023-03-02 20:13:25.000,2.0.0,1.0,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -230,2DMD dataset,,datasets,Apache-2.0,https://github.com/HSE-LAMBDA/ai4material_design/blob/main/docs/DATA.md,"Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of..",9,True,['material-defect'],HSE-LAMBDA/ai4material_design,https://github.com/HSE-LAMBDA/ai4material_design,2021-03-25 10:06:20,2023-11-21 11:30:42.000,2023-11-21 11:30:33,1118.0,,3.0,8.0,28.0,,12.0,6.0,,,,11.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -231,ai4material_design,,rep-learn,Apache-2.0,https://github.com/HSE-LAMBDA/ai4material_design,"Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of..",9,True,"['pretrained', 'material-defect']",HSE-LAMBDA/ai4material_design,https://github.com/HSE-LAMBDA/ai4material_design,2021-03-25 10:06:20,2023-11-21 11:30:42.000,2023-11-21 11:30:33,1118.0,,3.0,8.0,28.0,,12.0,6.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -232,MADICES Awesome Interoperability,,community,MIT,MADICES/MADICES.github.io/blob/main/docs/awesome_interoperability.md,Linked data interoperability resources of the Machine-actionable data interoperability for the chemical sciences..,9,False,['datasets'],MADICES/MADICES.github.io,https://github.com/MADICES/MADICES.github.io,2021-12-26 13:27:32,2024-07-10 07:36:51.000,2024-07-10 07:35:07,217.0,18.0,5.0,4.0,17.0,1.0,14.0,1.0,,,,10.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -233,Awesome Neural Geometry,,community,,https://github.com/neurreps/awesome-neural-geometry,"A curated collection of resources and research related to the geometry of representations in the brain, deep networks,..",8,True,"['educational', 'rep-learn']",neurreps/awesome-neural-geometry,https://github.com/neurreps/awesome-neural-geometry,2022-07-31 01:19:57,2024-08-13 14:39:08.000,2024-07-17 18:47:49,124.0,2.0,56.0,28.0,13.0,,1.0,887.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -234,molecularGNN_smiles,,rep-learn,Apache-2.0,https://github.com/masashitsubaki/molecularGNN_smiles,"The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius..",8,False,,masashitsubaki/molecularGNN_smiles,https://github.com/masashitsubaki/molecularGNN_smiles,2018-11-06 00:25:26,2020-11-28 02:04:45.000,2020-11-28 02:04:45,79.0,,75.0,6.0,,6.0,1.0,287.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -235,Awesome-Graph-Generation,,community,,https://github.com/yuanqidu/awesome-graph-generation,A curated list of up-to-date graph generation papers and resources.,8,True,['rep-learn'],yuanqidu/awesome-graph-generation,https://github.com/yuanqidu/awesome-graph-generation,2021-08-07 05:43:46,2024-06-24 01:56:37.000,2024-03-17 06:07:46,84.0,,17.0,7.0,2.0,,,255.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -236,RDKit Tutorials,,educational,https://github.com/rdkit/rdkit-tutorials/blob/master/LICENSE,https://github.com/rdkit/rdkit-tutorials,Tutorials to learn how to work with the RDKit.,8,False,,rdkit/rdkit-tutorials,https://github.com/rdkit/rdkit-tutorials,2016-10-07 03:34:01,2023-03-19 13:36:55.000,2023-03-19 13:36:55,68.0,,70.0,17.0,7.0,5.0,1.0,251.0,,,,5.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -237,QDF for molecule,,ml-esm,MIT,https://github.com/masashitsubaki/QuantumDeepField_molecule,"Quantum deep field: data-driven wave function, electron density generation, and energy prediction and extrapolation..",8,False,,masashitsubaki/QuantumDeepField_molecule,https://github.com/masashitsubaki/QuantumDeepField_molecule,2020-11-11 01:06:09,2021-02-20 03:46:18.000,2021-02-20 03:46:09,20.0,,40.0,4.0,,1.0,3.0,197.0,,,,1.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, -238,EquiformerV2,,rep-learn,MIT,https://github.com/atomicarchitects/equiformer_v2,[ICLR24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations.,8,True,,atomicarchitects/equiformer_v2,https://github.com/atomicarchitects/equiformer_v2,2023-06-21 07:09:58,2024-07-31 23:38:48.000,2024-07-16 05:51:23,16.0,4.0,25.0,5.0,1.0,15.0,2.0,185.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -239,BestPractices,,educational,MIT,https://github.com/anthony-wang/BestPractices,Things that you should (and should not) do in your Materials Informatics research.,8,True,,anthony-wang/BestPractices,https://github.com/anthony-wang/BestPractices,2020-05-05 19:41:25,2023-11-17 02:58:25.000,2023-11-17 02:58:25,17.0,,68.0,8.0,8.0,5.0,2.0,165.0,,,,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -240,G-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/G-SchNet,G-SchNet - a generative model for 3d molecular structures.,8,False,,atomistic-machine-learning/G-SchNet,https://github.com/atomistic-machine-learning/G-SchNet,2019-10-21 13:48:59,2023-03-24 12:05:41.000,2023-03-24 12:05:41,64.0,,25.0,7.0,,,10.0,129.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -241,ANI-1 Dataset,,datasets,MIT,https://github.com/isayev/ANI1_dataset,A data set of 20 million calculated off-equilibrium conformations for organic molecules.,8,False,,isayev/ANI1_dataset,https://github.com/isayev/ANI1_dataset,2017-08-07 20:08:46,2022-08-08 15:56:17.000,2022-08-08 15:56:17,25.0,,18.0,12.0,2.0,8.0,3.0,96.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -242,AIMNet,,ml-iap,MIT,https://github.com/aiqm/aimnet,Atoms In Molecules Neural Network Potential.,8,False,['single-paper'],aiqm/aimnet,https://github.com/aiqm/aimnet,2018-09-26 17:28:37,2019-11-21 23:49:01.000,2019-11-21 23:49:00,7.0,,24.0,10.0,2.0,4.0,,94.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -243,MoleculeNet Leaderboard,,datasets,MIT,https://github.com/deepchem/moleculenet,,8,False,['benchmarking'],deepchem/moleculenet,https://github.com/deepchem/moleculenet,2020-02-24 18:14:05,2021-04-29 19:51:06.000,2021-04-29 19:51:06,78.0,,19.0,5.0,15.0,24.0,5.0,87.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -244,Awesome Neural SBI,,community,MIT,https://github.com/smsharma/awesome-neural-sbi,Community-sourced list of papers and resources on neural simulation-based inference.,8,True,['active-learning'],smsharma/awesome-neural-sbi,https://github.com/smsharma/awesome-neural-sbi,2023-01-20 19:48:13,2024-06-17 04:24:32.000,2024-06-17 04:24:27,56.0,5.0,4.0,6.0,2.0,1.0,1.0,82.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -245,MACE-Jax,,ml-iap,MIT,https://github.com/ACEsuit/mace-jax,Equivariant machine learning interatomic potentials in JAX.,8,True,,ACEsuit/mace-jax,https://github.com/ACEsuit/mace-jax,2023-02-06 12:10:16,2023-10-04 08:07:35.000,2023-10-04 08:07:35,207.0,,5.0,11.0,1.0,3.0,3.0,58.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -246,graphite,,rep-learn,MIT,https://github.com/LLNL/graphite,A repository for implementing graph network models based on atomic structures.,8,True,,llnl/graphite,https://github.com/LLNL/graphite,2022-06-27 19:15:27,2024-08-08 04:10:45.000,2024-08-08 04:10:44,30.0,2.0,9.0,5.0,4.0,2.0,1.0,53.0,,,,2.0,,,11.0,11.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -247,HamGNN,,ml-dft,GPL-3.0,https://github.com/QuantumLab-ZY/HamGNN,An E(3) equivariant Graph Neural Network for predicting electronic Hamiltonian matrix.,8,True,"['rep-learn', 'magnetism', 'lang-c']",QuantumLab-ZY/HamGNN,https://github.com/QuantumLab-ZY/HamGNN,2023-07-14 12:20:27,2024-08-06 17:17:12.000,2024-08-06 17:16:48,63.0,22.0,12.0,5.0,,20.0,4.0,50.0,,,,1.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -248,DeeperGATGNN,,rep-learn,MIT,https://github.com/usccolumbia/deeperGATGNN,Scalable graph neural networks for materials property prediction.,8,True,,usccolumbia/deeperGATGNN,https://github.com/usccolumbia/deeperGATGNN,2021-09-29 17:31:02,2024-01-19 18:11:52.000,2024-01-19 18:11:38,25.0,,8.0,3.0,1.0,4.0,8.0,46.0,2022-03-08 02:14:28.000,1.0,1.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -249,Sketchmap,,unsupervised,GPL-3.0,https://github.com/lab-cosmo/sketchmap,Suite of programs to perform non-linear dimensionality reduction -- sketch-map in particular.,8,False,['lang-cpp'],lab-cosmo/sketchmap,https://github.com/lab-cosmo/sketchmap,2014-05-20 09:33:32,2024-02-20 20:57:41.000,2023-05-24 22:47:50,64.0,,10.0,31.0,1.0,4.0,5.0,44.0,,,,8.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -250,PyNEP,,ml-iap,MIT,https://github.com/bigd4/PyNEP,A python interface of the machine learning potential NEP used in GPUMD.,8,True,,bigd4/PyNEP,https://github.com/bigd4/PyNEP,2022-03-21 06:27:13,2024-06-01 09:06:22.000,2024-06-01 09:06:22,80.0,3.0,17.0,2.0,15.0,4.0,7.0,44.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -251,SIMPLE-NN v2,,ml-iap,GPL-3.0,https://github.com/MDIL-SNU/SIMPLE-NN_v2,SIMPLE-NN is an open package that constructs Behler-Parrinello-type neural-network interatomic potentials from ab..,8,True,,MDIL-SNU/SIMPLE-NN_v2,https://github.com/MDIL-SNU/SIMPLE-NN_v2,2021-03-02 09:36:49,2023-12-29 02:08:47.000,2023-12-29 02:08:47,504.0,,17.0,5.0,88.0,4.0,9.0,39.0,,,,13.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -252,SNAP,,ml-iap,BSD-3-Clause,https://github.com/materialsvirtuallab/snap,Repository for spectral neighbor analysis potential (SNAP) model development.,8,False,,materialsvirtuallab/snap,https://github.com/materialsvirtuallab/snap,2017-06-26 21:56:00,2020-06-30 05:20:37.000,2020-06-30 05:20:37,38.0,,17.0,11.0,1.0,1.0,3.0,36.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -253,Atomistic Adversarial Attacks,,ml-iap,MIT,https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks,Code for performing adversarial attacks on atomistic systems using NN potentials.,8,False,['probabilistic'],learningmatter-mit/Atomistic-Adversarial-Attacks,https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks,2021-03-28 17:39:52,2022-10-03 16:19:31.000,2022-10-03 16:19:29,33.0,,7.0,5.0,1.0,,1.0,30.0,2021-07-19 18:09:36.000,1.0.1,1.0,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -254,CGAT,,rep-learn,MIT,https://github.com/hyllios/CGAT,Crystal graph attention neural networks for materials prediction.,8,False,,hyllios/CGAT,https://github.com/hyllios/CGAT,2021-03-28 09:51:15,2023-07-18 12:04:35.000,2023-01-10 22:31:07,153.0,,8.0,3.0,1.0,,1.0,25.0,2023-07-18 12:04:35.000,0.1,1.0,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -255,ACE1.jl,,ml-iap,https://github.com/ACEsuit/ACE1.jl/blob/main/ASL.md,https://acesuit.github.io/,Atomic Cluster Expansion for Modelling Invariant Atomic Properties.,8,True,['lang-julia'],ACEsuit/ACE1.jl,https://github.com/ACEsuit/ACE1.jl,2022-01-14 19:52:49,2024-07-02 14:12:25.000,2024-07-02 14:12:23,558.0,1.0,7.0,5.0,30.0,22.0,24.0,20.0,,,,9.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -256,GElib,,math,MPL-2.0,https://github.com/risi-kondor/GElib,C++/CUDA library for SO(3) equivariant operations.,8,True,['lang-cpp'],risi-kondor/GElib,https://github.com/risi-kondor/GElib,2021-08-24 20:56:40,2024-08-06 20:45:38.000,2024-07-27 21:36:58,602.0,1.0,3.0,4.0,4.0,3.0,4.0,19.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -257,bVAE-IM,,generative,MIT,https://github.com/tsudalab/bVAE-IM,Implementation of Chemical Design with GPU-based Ising Machine.,8,False,"['qml', 'single-paper']",tsudalab/bVAE-IM,https://github.com/tsudalab/bVAE-IM,2023-03-01 08:26:56,2023-07-11 04:39:24.000,2023-07-11 04:39:24,39.0,,3.0,8.0,,,,11.0,2023-03-01 14:26:13.000,1.0.0,1.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -258,T-e3nn,,rep-learn,MIT,https://github.com/Hongyu-yu/T-e3nn,Time-reversal Euclidean neural networks based on e3nn.,8,False,['magnetism'],Hongyu-yu/T-e3nn,https://github.com/Hongyu-yu/T-e3nn,2022-11-21 14:49:45,2023-02-21 16:36:26.000,2023-02-21 16:36:25,2145.0,,,2.0,,,,8.0,,,,26.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -259,MLXDM,,ml-iap,MIT,https://github.com/RowleyGroup/MLXDM,A Neural Network Potential with Rigorous Treatment of Long-Range Dispersion https://doi.org/10.1039/D2DD00150K.,8,True,['long-range'],RowleyGroup/MLXDM,https://github.com/RowleyGroup/MLXDM,2022-05-03 17:47:26,2024-08-15 21:32:50.000,2024-08-15 21:32:12,53.0,18.0,2.0,5.0,,,,6.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -260,MEGNetSparse,,ml-iap,MIT,https://github.com/HSE-LAMBDA/MEGNetSparse,"A library imlementing a graph neural network with sparse representation from Code for Kazeev, N., Al-Maeeni, A.R.,..",8,False,['material-defect'],HSE-LAMBDA/MEGNetSparse,https://github.com/HSE-LAMBDA/MEGNetSparse,2023-07-19 08:17:42,2023-08-21 17:11:34.000,2023-08-21 17:11:25,19.0,,1.0,2.0,,,,1.0,2023-08-21 17:11:01.000,0.0.10,9.0,2.0,MEGNetSparse,,1.0,1.0,https://pypi.org/project/MEGNetSparse,2023-08-21 17:11:01.000,,51.0,51.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, -261,GEOM,,datasets,,https://github.com/learningmatter-mit/geom,GEOM: Energy-annotated molecular conformations.,7,False,['drug-discovery'],learningmatter-mit/geom,https://github.com/learningmatter-mit/geom,2020-06-03 17:58:37,2022-04-24 18:57:39.000,2022-04-24 18:57:39,95.0,,24.0,10.0,,2.0,11.0,189.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -262,tensorfieldnetworks,,rep-learn,MIT,https://github.com/tensorfieldnetworks/tensorfieldnetworks,Rotation- and translation-equivariant neural networks for 3D point clouds.,7,False,,tensorfieldnetworks/tensorfieldnetworks,https://github.com/tensorfieldnetworks/tensorfieldnetworks,2018-02-09 23:18:13,2020-01-07 17:22:16.000,2020-01-07 17:22:15,10.0,,28.0,9.0,2.0,1.0,2.0,151.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -263,A Highly Opinionated List of Open-Source Materials Informatics Resources,,community,MIT,https://github.com/ncfrey/resources,A Highly Opinionated List of Open Source Materials Informatics Resources.,7,False,,ncfrey/resources,https://github.com/ncfrey/resources,2020-11-17 23:47:07,2022-02-18 13:37:51.000,2022-02-18 13:37:51,8.0,,22.0,9.0,,,,116.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -264,PhysNet,,ml-iap,MIT,https://github.com/MMunibas/PhysNet,Code for training PhysNet models.,7,False,['electrostatics'],MMunibas/PhysNet,https://github.com/MMunibas/PhysNet,2019-03-28 09:05:22,2022-10-16 17:45:42.000,2020-12-07 11:09:20,4.0,,26.0,9.0,1.0,5.0,,88.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -265,DTNN,,rep-learn,MIT,https://github.com/atomistic-machine-learning/dtnn,Deep Tensor Neural Network.,7,False,,atomistic-machine-learning/dtnn,https://github.com/atomistic-machine-learning/dtnn,2017-03-10 14:40:05,2017-07-11 08:26:15.000,2017-07-11 08:25:39,9.0,,31.0,14.0,,,3.0,76.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -266,JAXChem,,general-tool,MIT,https://github.com/deepchem/jaxchem,JAXChem is a JAX-based deep learning library for complex and versatile chemical modeling.,7,False,,deepchem/jaxchem,https://github.com/deepchem/jaxchem,2020-05-11 18:54:41,2020-07-15 05:02:21.000,2020-07-15 04:55:41,96.0,,10.0,7.0,13.0,1.0,1.0,76.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -267,Cormorant,,rep-learn,https://github.com/risilab/cormorant/blob/master/LICENSE,https://github.com/risilab/cormorant,Codebase for Cormorant Neural Networks.,7,False,,risilab/cormorant,https://github.com/risilab/cormorant,2019-10-27 18:22:07,2022-05-11 12:49:05.000,2020-03-11 15:25:51,160.0,,10.0,6.0,1.0,3.0,,59.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -268,Awesome-Crystal-GNNs,,community,MIT,https://github.com/kdmsit/Awesome-Crystal-GNNs,This repository contains a collection of resources and papers on GNN Models on Crystal Solid State Materials.,7,True,,kdmsit/Awesome-Crystal-GNNs,https://github.com/kdmsit/Awesome-Crystal-GNNs,2022-11-15 11:12:18,2024-06-16 16:02:41.000,2024-06-16 16:02:37,34.0,2.0,7.0,4.0,,,,54.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -269,cG-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/cG-SchNet,cG-SchNet - a conditional generative neural network for 3d molecular structures.,7,False,,atomistic-machine-learning/cG-SchNet,https://github.com/atomistic-machine-learning/cG-SchNet,2021-12-02 15:35:18,2023-03-24 12:09:56.000,2023-03-24 12:09:56,28.0,,14.0,3.0,,,3.0,51.0,2022-02-21 13:36:41.000,1.0,1.0,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -270,uncertainty_benchmarking,,general-tool,,https://github.com/ulissigroup/uncertainty_benchmarking,Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions.,7,False,"['benchmarking', 'probabilistic']",ulissigroup/uncertainty_benchmarking,https://github.com/ulissigroup/uncertainty_benchmarking,2019-08-28 19:39:28,2021-06-07 23:29:39.000,2021-06-07 23:27:19,265.0,,7.0,6.0,1.0,,,38.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -271,AdsorbML,,rep-learn,MIT,https://github.com/Open-Catalyst-Project/AdsorbML,,7,False,"['surface-science', 'single-paper']",Open-Catalyst-Project/AdsorbML,https://github.com/Open-Catalyst-Project/AdsorbML,2022-11-30 01:38:20,2024-05-07 21:54:19.000,2023-07-31 16:28:09,56.0,,4.0,7.0,11.0,3.0,1.0,35.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -272,torchchem,,general-tool,MIT,https://github.com/deepchem/torchchem,An experimental repo for experimenting with PyTorch models.,7,False,,deepchem/torchchem,https://github.com/deepchem/torchchem,2020-03-07 17:06:44,2023-03-24 23:13:19.000,2020-05-01 20:12:23,49.0,,13.0,8.0,27.0,5.0,1.0,34.0,,,,5.0,,,1.0,1.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -273,chemlift,,language-models,MIT,https://github.com/lamalab-org/chemlift,Language-interfaced fine-tuning for chemistry.,7,True,,lamalab-org/chemlift,https://github.com/lamalab-org/chemlift,2023-07-10 06:54:07,2023-11-30 10:47:50.000,2023-10-14 16:50:14,36.0,,3.0,1.0,1.0,11.0,7.0,31.0,2023-11-30 19:42:07.000,0.0.1,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -274,ChargE3Net,,ml-dft,MIT,https://github.com/AIforGreatGood/charge3net,Higher-order equivariant neural networks for charge density prediction in materials.,7,True,['rep-learn'],AIforGreatGood/charge3net,https://github.com/AIforGreatGood/charge3net,2023-12-16 13:54:56,2024-08-15 14:35:44.000,2024-08-15 14:35:27,12.0,8.0,6.0,5.0,1.0,,3.0,28.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -275,Mat2Spec,,ml-dft,MIT,https://github.com/gomes-lab/Mat2Spec,Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings.,7,False,['spectroscopy'],gomes-lab/Mat2Spec,https://github.com/gomes-lab/Mat2Spec,2022-01-17 11:45:57,2022-04-17 17:12:29.000,2022-04-17 17:12:29,8.0,,10.0,,,,,27.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -276,escnn_jax,,rep-learn,https://github.com/emilemathieu/escnn_jax/blob/master/LICENSE,https://github.com/emilemathieu/escnn_jax,Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/.,7,False,,emilemathieu/escnn_jax,https://github.com/emilemathieu/escnn_jax,2023-06-15 09:45:45,2023-06-28 14:40:32.000,2023-06-28 14:39:56,203.0,,2.0,,,,,26.0,,,,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -277,LLM-Prop,,language-models,MIT,https://github.com/vertaix/LLM-Prop,A repository for the LLM-Prop implementation.,7,True,,vertaix/LLM-Prop,https://github.com/vertaix/LLM-Prop,2022-10-16 19:15:21,2024-04-26 14:20:54.000,2024-04-26 14:20:54,175.0,,5.0,2.0,,1.0,1.0,25.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -278,Libnxc,,ml-dft,MPL-2.0,https://github.com/semodi/libnxc,A library for using machine-learned exchange-correlation functionals for density-functional theory.,7,False,"['lang-cpp', 'lang-fortran']",semodi/libnxc,https://github.com/semodi/libnxc,2020-07-01 18:21:50,2021-09-18 14:53:52.000,2021-08-14 16:26:32,100.0,,4.0,2.0,3.0,13.0,3.0,16.0,,,2.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -279,COSMO Software Cookbook,,educational,BSD-3-Clause,https://github.com/lab-cosmo/atomistic-cookbook,The COSMO cookbook contains recipes for atomic-scale modelling for materials and molecules.,7,True,,lab-cosmo/software-cookbook,https://github.com/lab-cosmo/atomistic-cookbook,2023-05-23 10:33:47,2024-08-19 08:07:41.000,2024-08-14 17:43:05,69.0,5.0,1.0,16.0,57.0,2.0,10.0,12.0,,,,9.0,,,,,,,,,,,,,3.0,,,,,,,lab-cosmo/atomistic-cookbook,,,,,,,,,,,,,, -280,NICE,,rep-eng,MIT,https://github.com/lab-cosmo/nice,NICE (N-body Iteratively Contracted Equivariants) is a set of tools designed for the calculation of invariant and..,7,True,,lab-cosmo/nice,https://github.com/lab-cosmo/nice,2020-07-03 08:47:41,2024-04-15 14:39:34.000,2024-04-15 14:39:33,233.0,,3.0,17.0,7.0,2.0,1.0,12.0,,,1.0,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -281,rxngenerator,,generative,MIT,https://github.com/tsudalab/rxngenerator,A generative model for molecular generation via multi-step chemical reactions.,7,False,,tsudalab/rxngenerator,https://github.com/tsudalab/rxngenerator,2021-06-18 07:44:53,2024-07-24 05:27:21.000,2022-08-09 07:21:05,16.0,,3.0,9.0,2.0,1.0,,11.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -282,AIS Square,,datasets,LGPL-3.0,https://github.com/deepmodeling/AIS-Square,"A collaborative and open-source platform for sharing AI for Science datasets, models, and workflows. Home of the..",7,True,"['community', 'model-repository']",deepmodeling/AIS-Square,https://github.com/deepmodeling/AIS-Square,2022-09-13 09:52:30,2024-08-16 07:47:57.000,2023-12-06 03:06:55,469.0,,8.0,8.0,210.0,5.0,1.0,10.0,,,,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -283,rho_learn,,ml-dft,MIT,https://github.com/jwa7/rho_learn,A proof-of-concept framework for torch-based learning of the electron density and related scalar fields.,7,False,,jwa7/rho_learn,https://github.com/jwa7/rho_learn,2023-03-27 16:59:34,2024-08-16 10:00:52.000,2024-03-20 15:20:39,111.0,,1.0,,4.0,,,3.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -284,MAChINE,,educational,MIT,https://github.com/aimat-lab/MAChINE,Client-Server Web App to introduce usage of ML in materials science to beginners.,7,False,,aimat-lab/MAChINE,https://github.com/aimat-lab/MAChINE,2023-04-17 14:29:06,2023-09-29 14:20:12.000,2023-09-29 10:20:31,1026.0,,,,7.0,9.0,23.0,1.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -285,ML4pXRDs,,rep-learn,MIT,https://github.com/aimat-lab/ML4pXRDs,Contains code to train neural networks based on simulated powder XRDs from synthetic crystals.,7,False,"['xrd', 'single-paper']",aimat-lab/ML4pXRDs,https://github.com/aimat-lab/ML4pXRDs,2022-12-01 16:24:29,2023-07-14 08:17:06.000,2023-07-14 08:17:04,1320.0,,,3.0,,,,,2023-03-22 11:04:31.000,1.0,1.0,,,,,,,,,,0.0,,,,3.0,,,,,,2.0,,,,,,,,,,,,,,, -286,The Collection of Database and Dataset Resources in Materials Science,,community,,https://github.com/sedaoturak/data-resources-for-materials-science,"A list of databases, datasets and books/handbooks where you can find materials properties for machine learning..",6,True,['datasets'],sedaoturak/data-resources-for-materials-science,https://github.com/sedaoturak/data-resources-for-materials-science,2021-02-20 06:38:45,2024-06-07 15:51:11.000,2024-06-07 15:51:11,30.0,1.0,40.0,12.0,2.0,1.0,1.0,249.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -287,DeepH-E3,,ml-dft,MIT,https://github.com/Xiaoxun-Gong/DeepH-E3,General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian.,6,False,['magnetism'],Xiaoxun-Gong/DeepH-E3,https://github.com/Xiaoxun-Gong/DeepH-E3,2023-03-16 11:25:58,2023-04-04 13:27:01.000,2023-04-04 13:26:27,16.0,,16.0,6.0,,9.0,6.0,61.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -288,Applied AI for Materials,,educational,,https://github.com/WardLT/applied-ai-for-materials,Course materials for Applied AI for Materials Science and Engineering.,6,False,,WardLT/applied-ai-for-materials,https://github.com/WardLT/applied-ai-for-materials,2020-10-12 19:39:06,2022-03-12 02:26:58.000,2022-03-12 02:26:41,107.0,,31.0,4.0,13.0,5.0,,58.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -289,DeepDFT,,ml-dft,MIT,https://github.com/peterbjorgensen/DeepDFT,Official implementation of DeepDFT model.,6,False,,peterbjorgensen/DeepDFT,https://github.com/peterbjorgensen/DeepDFT,2020-11-03 11:51:15,2023-02-28 15:37:49.000,2023-02-28 15:37:37,128.0,,7.0,1.0,,,5.0,54.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -290,ANI-1x Datasets,,datasets,MIT,https://github.com/aiqm/ANI1x_datasets,"The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for organic molecules.",6,False,,aiqm/ANI1x_datasets,https://github.com/aiqm/ANI1x_datasets,2019-09-17 18:19:28,2022-04-11 17:25:55.000,2022-04-11 17:25:55,12.0,,5.0,5.0,,4.0,3.0,53.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -291,COMP6 Benchmark dataset,,datasets,MIT,https://github.com/isayev/COMP6,COMP6 Benchmark dataset for ML potentials.,6,False,,isayev/COMP6,https://github.com/isayev/COMP6,2017-12-29 16:58:35,2018-07-09 23:56:35.000,2018-07-09 23:56:34,27.0,,4.0,5.0,,2.0,1.0,39.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -292,MACE-Layer,,rep-learn,MIT,https://github.com/ACEsuit/mace-layer,Higher order equivariant graph neural networks for 3D point clouds.,6,False,,ACEsuit/mace-layer,https://github.com/ACEsuit/mace-layer,2022-11-09 17:03:41,2023-06-27 15:32:49.000,2023-06-06 10:09:58,19.0,,6.0,5.0,2.0,1.0,,33.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -293,MACE-tutorials,,educational,MIT,https://github.com/ilyes319/mace-tutorials,Another set of tutorials for the MACE interatomic potential by one of the authors.,6,True,"['ml-iap', 'rep-learn', 'md']",ilyes319/mace-tutorials,https://github.com/ilyes319/mace-tutorials,2023-09-11 18:09:18,2024-07-16 12:45:45.000,2024-07-16 12:45:42,7.0,2.0,9.0,3.0,,1.0,,31.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -294,charge_transfer_nnp,,rep-learn,MIT,https://github.com/pfnet-research/charge_transfer_nnp,Graph neural network potential with charge transfer.,6,False,['electrostatics'],pfnet-research/charge_transfer_nnp,https://github.com/pfnet-research/charge_transfer_nnp,2022-04-06 01:48:18,2022-04-06 01:53:35.000,2022-04-06 01:53:22,1.0,,8.0,12.0,,,,28.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -295,MLIP-3,,ml-iap,BSD-2-Clause,https://gitlab.com/ashapeev/mlip-3,MLIP-3: Active learning on atomic environments with Moment Tensor Potentials (MTP).,6,False,['lang-cpp'],,,2023-04-24 14:05:53,2023-04-24 14:05:53.000,,,,6.0,,,23.0,6.0,25.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,ashapeev/mlip-3,https://gitlab.com/ashapeev/mlip-3,, -296,GLAMOUR,,rep-learn,MIT,https://github.com/learningmatter-mit/GLAMOUR,Graph Learning over Macromolecule Representations.,6,False,['single-paper'],learningmatter-mit/GLAMOUR,https://github.com/learningmatter-mit/GLAMOUR,2021-08-20 18:16:40,2022-12-31 17:56:21.000,2022-12-31 17:56:21,14.0,,6.0,3.0,,,8.0,20.0,2021-08-23 18:58:52.000,0.1,1.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -297,SA-GPR,,rep-eng,LGPL-3.0,https://github.com/dilkins/TENSOAP,Public repository for symmetry-adapted Gaussian Process Regression (SA-GPR).,6,True,['lang-c'],dilkins/TENSOAP,https://github.com/dilkins/TENSOAP,2020-05-04 14:19:01,2024-07-23 13:03:45.000,2024-07-23 13:03:44,26.0,1.0,13.0,3.0,10.0,2.0,5.0,19.0,2020-12-17 16:51:47.000,2020.0,1.0,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -298,EquivariantOperators.jl,,math,MIT,https://github.com/aced-differentiate/EquivariantOperators.jl,This package is deprecated. Functionalities are migrating to Porcupine.jl.,6,True,['lang-julia'],aced-differentiate/EquivariantOperators.jl,https://github.com/aced-differentiate/EquivariantOperators.jl,2021-11-29 03:36:21,2023-09-27 18:34:44.000,2023-09-27 18:34:44,62.0,,,4.0,,,,18.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -299,CatGym,,reinforcement-learning,GPL,https://github.com/ulissigroup/catgym,Surface segregation using Deep Reinforcement Learning.,6,False,,ulissigroup/catgym,https://github.com/ulissigroup/catgym,2019-08-06 19:25:27,2021-08-30 17:05:36.000,2021-08-30 17:05:32,162.0,,2.0,4.0,,2.0,,11.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -300,testing-framework,,ml-iap,,https://github.com/libAtoms/testing-framework,The purpose of this repository is to aid the testing of a large number of interatomic potentials for a variety of..,6,False,['benchmarking'],libAtoms/testing-framework,https://github.com/libAtoms/testing-framework,2020-03-04 11:43:15,2022-02-10 17:23:46.000,2022-02-10 17:23:46,225.0,,6.0,16.0,10.0,5.0,3.0,11.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -301,PANNA,,ml-iap,MIT,https://gitlab.com/PANNAdevs/panna,A package to train and validate all-to-all connected network models for BP[1] and modified-BP[2] type local atomic..,6,False,['benchmarking'],,,2018-11-09 10:47:48,2018-11-09 10:47:48.000,,,,10.0,,,,,9.0,,,2.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,PANNAdevs/panna,https://gitlab.com/PANNAdevs/panna,, -302,ML for catalysis tutorials,,educational,MIT,https://github.com/ulissigroup/ml_catalysis_tutorials,A jupyter book repo for tutorial on how to use OCP ML models for catalysis.,6,False,,ulissigroup/ml_catalysis_tutorials,https://github.com/ulissigroup/ml_catalysis_tutorials,2022-10-28 20:37:30,2022-10-31 18:06:07.000,2022-10-31 17:49:25,40.0,,1.0,4.0,,,,8.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -303,COSMO Toolbox,,math,,https://github.com/lab-cosmo/toolbox,Assorted libraries and utilities for atomistic simulation analysis.,6,True,['lang-cpp'],lab-cosmo/toolbox,https://github.com/lab-cosmo/toolbox,2014-05-20 11:23:13,2024-03-19 13:27:28.000,2024-03-19 13:27:02,107.0,,5.0,27.0,1.0,,,7.0,,,,9.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -304,fplib,,rep-eng,MIT,https://github.com/zhuligs/fplib,a fingerprint library.,6,False,"['lang-c', 'single-paper']",zhuligs/fplib,https://github.com/zhuligs/fplib,2015-09-07 08:18:27,2022-02-09 05:31:21.000,2022-02-09 05:31:12,37.0,,2.0,3.0,,,3.0,7.0,2021-02-03 21:40:23.000,pub,1.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -305,SOAPxx,,rep-eng,GPL-2.0,https://github.com/capoe/soapxx,A SOAP implementation.,6,False,['lang-cpp'],capoe/soapxx,https://github.com/capoe/soapxx,2016-03-29 10:00:00,2020-03-27 13:47:44.000,2020-03-27 13:47:36,289.0,,3.0,3.0,1.0,,2.0,7.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -306,Equisolve,,general-tool,BSD-3-Clause,https://github.com/lab-cosmo/equisolve,A ML toolkit package utilizing the metatensor data format to build models for the prediction of equivariant properties..,6,True,['ml-iap'],lab-cosmo/equisolve,https://github.com/lab-cosmo/equisolve,2022-10-04 15:29:19,2023-10-27 10:03:59.000,2023-10-27 09:55:17,55.0,,1.0,17.0,43.0,19.0,4.0,5.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -307,MEGAN: Multi Explanation Graph Attention Student,,xai,MIT,https://github.com/aimat-lab/graph_attention_student,Minimal implementation of graph attention student model architecture.,6,True,['rep-learn'],aimat-lab/graph_attention_student,https://github.com/aimat-lab/graph_attention_student,2022-07-28 06:22:50,2024-08-19 08:43:44.000,2024-08-19 08:40:19,92.0,13.0,1.0,3.0,1.0,,2.0,5.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,1.0,,,,,,,,, -308,Cephalo,,language-models,Apache-2.0,https://github.com/lamm-mit/Cephalo,Multimodal Vision-Language Models for Bio-Inspired Materials Analysis and Design.,6,True,"['generative', 'multimodal', 'pretrained']",lamm-mit/Cephalo,https://github.com/lamm-mit/Cephalo,2024-05-28 12:29:13,2024-07-23 09:27:58.000,2024-07-23 09:27:57,24.0,24.0,1.0,1.0,,,,5.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -309,cnine,,math,,https://github.com/risi-kondor/cnine,Cnine tensor library.,6,False,['lang-cpp'],risi-kondor/cnine,https://github.com/risi-kondor/cnine,2022-10-07 20:54:54,2024-08-19 04:16:23.000,2024-08-09 03:21:10,381.0,10.0,4.0,2.0,7.0,,1.0,4.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,https://risi-kondor.github.io/cnine/, -310,soap_turbo,,rep-eng,https://github.com/libAtoms/soap_turbo/blob/master/LICENSE.md,https://github.com/libAtoms/soap_turbo,soap_turbo comprises a series of libraries to be used in combination with QUIP/GAP and TurboGAP.,6,False,['lang-fortran'],libAtoms/soap_turbo,https://github.com/libAtoms/soap_turbo,2021-03-19 15:20:25,2024-06-28 11:53:50.000,2023-05-24 09:42:00,36.0,,8.0,8.0,,5.0,3.0,4.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -311,KSR-DFT,,ml-dft,Apache-2.0,https://github.com/pedersor/ksr_dft,Kohn-Sham regularizer for machine-learned DFT functionals.,6,False,,pedersor/ksr_dft,https://github.com/pedersor/ksr_dft,2023-03-01 17:24:48,2023-03-04 07:20:22.000,2023-03-04 07:20:18,466.0,,,1.0,,,,4.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -312,pyLODE,,rep-eng,Apache-2.0,https://github.com/ceriottm/lode,Pythonic implementation of LOng Distance Equivariants.,6,False,['electrostatics'],ceriottm/lode,https://github.com/ceriottm/lode,2022-01-19 17:01:38,2023-07-05 09:57:29.000,2023-07-05 09:57:14,241.0,,1.0,2.0,,1.0,,3.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -313,ACEpsi.jl,,ml-wft,MIT,https://github.com/ACEsuit/ACEpsi.jl,ACE wave function parameterizations.,6,False,"['rep-eng', 'lang-julia']",ACEsuit/ACEpsi.jl,https://github.com/ACEsuit/ACEpsi.jl,2022-10-21 03:51:18,2024-04-12 06:18:19.000,2023-10-05 21:21:35,162.0,,,4.0,16.0,5.0,4.0,2.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -314,OPTIMADE providers dashboard,,datasets,,https://www.optimade.org/providers-dashboard/,A dashboard of known providers.,6,False,,Materials-Consortia/providers-dashboard,https://github.com/Materials-Consortia/providers-dashboard,2020-06-17 16:15:07,2024-08-19 06:31:46.000,2024-08-01 23:27:42,139.0,21.0,3.0,19.0,141.0,10.0,18.0,1.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -315,Computational Autonomy for Materials Discovery (CAMD),,materials-discovery,Apache-2.0,https://github.com/ulissigroup/CAMD,Agent-based sequential learning software for materials discovery.,6,False,,ulissigroup/CAMD,https://github.com/ulissigroup/CAMD,2023-01-10 19:42:57,2023-01-10 19:49:35.000,2023-01-10 19:49:13,1336.0,,,1.0,,,,1.0,,,,17.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, -316,ACE1Pack.jl,,ml-iap,MIT,https://github.com/ACEsuit/ACE1pack.jl,"Provides convenience functionality for the usage of ACE1.jl, ACEfit.jl, JuLIP.jl for fitting interatomic potentials..",6,True,['lang-julia'],ACEsuit/ACE1pack.jl,https://github.com/ACEsuit/ACE1pack.jl,2023-08-21 16:25:00,2023-08-21 16:30:19.000,2023-08-21 15:48:54,547.0,,,1.0,,,,,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,https://acesuit.github.io/ACE1pack.jl, -317,AMP,,rep-eng,,https://bitbucket.org/andrewpeterson/amp/,Amp is an open-source package designed to easily bring machine-learning to atomistic calculations.,6,False,,,,,2023-01-25 17:30:41.112,,,,25.0,,,,,,2023-01-25 17:30:41.112,1.0.1,3.0,,amp-atomistics,,,,https://pypi.org/project/amp-atomistics,2023-01-25 17:30:41.112,,78.0,78.0,,,,3.0,,,,,,,,,,,,,,,,,,,,https://amp.readthedocs.io/, -318,COATI,,generative,Apache 2.0,https://github.com/terraytherapeutics/COATI,COATI: multi-modal contrastive pre-training for representing and traversing chemical space.,5,True,"['drug-discovery', 'multimodal', 'pretrained', 'rep-learn']",terraytherapeutics/COATI,https://github.com/terraytherapeutics/COATI,2023-08-11 14:56:39,2024-03-23 18:06:26.000,2024-03-23 18:06:26,16.0,,5.0,2.0,6.0,1.0,2.0,89.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -319,AI4Science101,,educational,,https://github.com/deepmodeling/AI4Science101,AI for Science.,5,False,,deepmodeling/AI4Science101,https://github.com/deepmodeling/AI4Science101,2022-06-19 02:26:48,2024-04-11 02:15:55.000,2022-09-04 02:06:18,139.0,,13.0,9.0,29.0,2.0,1.0,83.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -320,crystal-text-llm,,language-models,CC-BY-NC-4.0,https://github.com/facebookresearch/crystal-text-llm,Large language models to generate stable crystals.,5,True,['materials-discovery'],facebookresearch/crystal-text-llm,https://github.com/facebookresearch/crystal-text-llm,2024-02-05 22:29:12,2024-06-18 17:10:52.000,2024-06-18 17:10:52,13.0,2.0,10.0,5.0,3.0,7.0,2.0,63.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -321,The Perovskite Database Project,,datasets,,https://github.com/Jesperkemist/perovskitedatabase,"Perovskite Database Project aims at making all perovskite device data, both past and future, available in a form..",5,True,['community'],Jesperkemist/perovskitedatabase,https://github.com/Jesperkemist/perovskitedatabase,2021-01-17 14:26:45,2024-03-07 11:09:21.000,2024-03-07 11:09:17,44.0,,18.0,3.0,7.0,1.0,,58.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -322,SchNOrb,,ml-wft,MIT,https://github.com/atomistic-machine-learning/SchNOrb,Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions.,5,False,,atomistic-machine-learning/SchNOrb,https://github.com/atomistic-machine-learning/SchNOrb,2019-09-17 12:41:48,2019-09-17 14:31:47.000,2019-09-17 14:31:19,2.0,,19.0,5.0,,1.0,,58.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -323,Machine Learning for Materials Hard and Soft,,educational,,https://github.com/CompPhysVienna/MLSummerSchoolVienna2022,ESI-DCAFM-TACO-VDSP Summer School on Machine Learning for Materials Hard and Soft.,5,False,,CompPhysVienna/MLSummerSchoolVienna2022,https://github.com/CompPhysVienna/MLSummerSchoolVienna2022,2022-07-01 08:42:41,2022-07-22 08:10:24.000,2022-07-22 08:10:24,49.0,,20.0,1.0,14.0,,,34.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -324,Joint Multidomain Pre-Training (JMP),,uip,CC-BY-NC-4.0,https://github.com/facebookresearch/JMP,Code for From Molecules to Materials Pre-training Large Generalizable Models for Atomic Property Prediction.,5,True,"['pretrained', 'ml-iap', 'general-tool']",facebookresearch/JMP,https://github.com/facebookresearch/JMP,2024-03-14 23:10:10,2024-06-20 04:11:08.000,2024-05-07 08:19:12,1.0,,4.0,4.0,1.0,1.0,,32.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -325,xDeepH,,ml-dft,LGPL-3.0,https://github.com/mzjb/xDeepH,Extended DeepH (xDeepH) method for magnetic materials.,5,False,"['magnetism', 'lang-julia']",mzjb/xDeepH,https://github.com/mzjb/xDeepH,2023-02-23 12:56:49,2023-06-14 11:44:53.000,2023-06-14 11:44:46,4.0,,3.0,3.0,,1.0,,30.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -326,Autobahn,,rep-learn,MIT,https://github.com/risilab/Autobahn,Repository for Autobahn: Automorphism Based Graph Neural Networks.,5,False,,risilab/Autobahn,https://github.com/risilab/Autobahn,2021-03-02 01:14:40,2022-03-01 21:04:09.000,2022-03-01 21:04:04,11.0,,2.0,5.0,,,,30.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -327,milad,,rep-eng,GPL-3.0,https://github.com/muhrin/milad,Moment Invariants Local Atomic Descriptor.,5,False,['generative'],muhrin/milad,https://github.com/muhrin/milad,2020-04-23 09:14:24,2022-12-03 10:40:05.000,2022-12-03 10:39:59,110.0,,1.0,4.0,,,,29.0,,,,1.0,,,2.0,2.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -328,SciBot,,language-models,,https://github.com/CFN-softbio/SciBot,SciBot is a simple demo of building a domain-specific chatbot for science.,5,True,['ai-agent'],CFN-softbio/SciBot,https://github.com/CFN-softbio/SciBot,2023-06-12 12:41:44,2024-04-19 18:34:24.000,2024-04-19 18:17:00,22.0,,8.0,6.0,,,,28.0,,,,1.0,,,1.0,1.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -329,ML-DFT,,ml-dft,MIT,https://github.com/MihailBogojeski/ml-dft,A package for density functional approximation using machine learning.,5,False,,MihailBogojeski/ml-dft,https://github.com/MihailBogojeski/ml-dft,2020-09-14 22:15:56,2020-09-18 16:36:30.000,2020-09-18 16:36:30,9.0,,7.0,2.0,,1.0,1.0,23.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -330,Coarse-Graining-Auto-encoders,,unsupervised,,https://github.com/learningmatter-mit/Coarse-Graining-Auto-encoders,Implementation of coarse-graining Autoencoders.,5,False,['single-paper'],learningmatter-mit/Coarse-Graining-Auto-encoders,https://github.com/learningmatter-mit/Coarse-Graining-Auto-encoders,2019-09-16 15:27:57,2019-08-16 21:39:34.000,2019-08-16 21:39:33,14.0,,7.0,6.0,,,,21.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -331,CSPML (crystal structure prediction with machine learning-based element substitution),,materials-discovery,,https://github.com/Minoru938/CSPML,Original implementation of CSPML.,5,True,['structure-prediction'],minoru938/cspml,https://github.com/Minoru938/CSPML,2022-01-15 10:59:27,2024-07-09 12:40:53.000,2024-07-09 12:40:53,23.0,16.0,8.0,2.0,,2.0,1.0,18.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -332,NequIP-JAX,,ml-iap,,https://github.com/mariogeiger/nequip-jax,JAX implementation of the NequIP interatomic potential.,5,True,,mariogeiger/nequip-jax,https://github.com/mariogeiger/nequip-jax,2023-03-08 04:18:28,2023-11-01 20:35:48.000,2023-11-01 20:35:44,39.0,,3.0,1.0,2.0,1.0,,17.0,2023-06-22 22:36:36.000,1.1.0,3.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -333,FieldSchNet,,rep-learn,MIT,https://github.com/atomistic-machine-learning/field_schnet,Deep neural network for molecules in external fields.,5,False,,atomistic-machine-learning/field_schnet,https://github.com/atomistic-machine-learning/field_schnet,2020-11-18 10:26:59,2022-05-19 09:28:38.000,2022-05-19 09:28:38,26.0,,4.0,2.0,1.0,1.0,,16.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -334,Does this material exist?,,community,MIT,https://thismaterialdoesnotexist.com/,Vote on whether you think predicted crystal structures could be synthesised.,5,True,"['for-fun', 'materials-discovery']",ml-evs/this-material-does-not-exist,https://github.com/ml-evs/this-material-does-not-exist,2023-12-01 18:16:28,2024-07-29 09:50:18.000,2024-04-10 12:32:06,16.0,,3.0,2.0,2.0,2.0,,15.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -335,3DSC Database,,datasets,https://github.com/aimat-lab/3DSC/blob/main/LICENSE.md,https://github.com/aimat-lab/3DSC,Repo for the paper publishing the superconductor database with 3D crystal structures.,5,True,"['superconductors', 'materials-discovery']",aimat-lab/3DSC,https://github.com/aimat-lab/3DSC,2021-11-02 09:07:57,2024-01-08 09:21:11.000,2024-01-08 09:21:11,53.0,,4.0,2.0,,1.0,,15.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -336,SCFNN,,rep-learn,MIT,https://github.com/andy90/SCFNN,Self-consistent determination of long-range electrostatics in neural network potentials.,5,False,"['lang-cpp', 'electrostatics', 'single-paper']",andy90/SCFNN,https://github.com/andy90/SCFNN,2021-09-22 12:02:00,2022-01-30 02:29:03.000,2022-01-24 09:40:40,10.0,,8.0,2.0,,,,15.0,2022-01-30 02:29:04.000,1.0.0,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -337,CraTENet,,rep-learn,MIT,https://github.com/lantunes/CraTENet,An attention-based deep neural network for thermoelectric transport properties.,5,False,['transport-phenomena'],lantunes/CraTENet,https://github.com/lantunes/CraTENet,2022-06-30 10:40:06,2023-04-05 01:13:22.000,2023-04-05 01:13:11,24.0,,1.0,1.0,,,,13.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -338,ACEHAL,,active-learning,,https://github.com/ACEsuit/ACEHAL,Hyperactive Learning (HAL) Python interface for building Atomic Cluster Expansion potentials.,5,True,['lang-julia'],ACEsuit/ACEHAL,https://github.com/ACEsuit/ACEHAL,2023-02-24 17:33:47,2023-10-01 12:19:41.000,2023-09-21 21:50:43,121.0,,7.0,5.0,15.0,4.0,6.0,11.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -339,BERT-PSIE-TC,,language-models,MIT,https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC,A dataset of Curie temperatures automatically extracted from scientific literature with the use of the BERT-PSIE..,5,True,['magnetism'],StefanoSanvitoGroup/BERT-PSIE-TC,https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC,2023-01-25 10:27:26,2023-08-18 11:47:45.000,2023-08-18 12:48:31,36.0,,3.0,1.0,,,,11.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -340,SOMD,,md,AGPL-3.0,https://github.com/initqp/somd,Molecular dynamics package designed for the SIESTA DFT code.,5,True,"['ml-iap', 'active-learning']",initqp/somd,https://github.com/initqp/somd,2023-03-09 19:00:41,2024-08-17 16:10:44.000,2024-08-17 16:07:39,303.0,4.0,2.0,1.0,11.0,,1.0,11.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -341,QMLearn,,ml-esm,MIT,http://qmlearn.rutgers.edu/,Quantum Machine Learning by learning one-body reduced density matrices in the AO basis...,5,False,,,,2022-02-15 13:42:13,2022-02-15 13:42:13.000,,,,3.0,,,,,11.0,,,0.0,,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,pavanello-research-group/qmlearn,https://gitlab.com/pavanello-research-group/qmlearn,, -342,SciGlass,,datasets,MIT,https://github.com/drcassar/SciGlass,The database contains a vast set of data on the properties of glass materials.,5,True,,drcassar/SciGlass,https://github.com/drcassar/SciGlass,2019-06-19 19:36:32,2023-08-27 13:46:44.000,2023-08-27 13:46:44,28.0,,3.0,1.0,,,,10.0,2023-08-27 13:48:09.000,2.0.1,1.0,2.0,,,,,,,,,1.0,,,,3.0,,,,,,16.0,,,,,,,,,,,,,,, -343,InfGCN for Electron Density Estimation,,ml-dft,MIT,https://github.com/ccr-cheng/InfGCN-pytorch,Official implementation of the NeurIPS 23 spotlight paper of InfGCN.,5,True,['rep-learn'],ccr-cheng/infgcn-pytorch,https://github.com/ccr-cheng/InfGCN-pytorch,2023-10-01 21:21:40,2023-12-05 01:31:19.000,2023-12-05 01:31:14,3.0,,3.0,1.0,,,3.0,10.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -344,GN-MM,,ml-iap,MIT,https://gitlab.com/zaverkin_v/gmnn,The Gaussian Moment Neural Network (GM-NN) package developed for large-scale atomistic simulations employing atomistic..,5,False,"['active-learning', 'md', 'rep-eng', 'magnetism']",,,2021-09-19 15:56:31,2021-09-19 15:56:31.000,,,,4.0,,,,,10.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,zaverkin_v/gmnn,https://gitlab.com/zaverkin_v/gmnn,, -345,MAPI_LLM,,language-models,MIT,https://github.com/maykcaldas/MAPI_LLM,A LLM application developed during the LLM March MADNESS Hackathon https://doi.org/10.1039/D3DD00113J.,5,True,"['ai-agent', 'dataset']",maykcaldas/MAPI_LLM,https://github.com/maykcaldas/MAPI_LLM,2023-03-30 04:24:54,2024-04-20 03:16:17.000,2024-04-11 22:22:28,31.0,,2.0,1.0,7.0,,,8.0,2023-06-29 18:48:44.000,0.0.1,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -346,EGraFFBench,,rep-learn,,https://github.com/M3RG-IITD/MDBENCHGNN,,5,False,"['single-paper', 'benchmarking', 'ml-iap']",M3RG-IITD/MDBENCHGNN,https://github.com/M3RG-IITD/MDBENCHGNN,2023-07-06 18:15:34,2023-11-19 05:16:12.000,2023-11-19 05:14:44,161.0,,,,,4.0,,8.0,2023-07-16 05:46:38.000,0.1.0,1.0,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -347,GDB-9-Ex9 and ORNL_AISD-Ex,,datasets,,https://github.com/ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python,Distributed computing workflow for generation and analysis of large scale molecular datasets obtained running multi-..,5,True,,ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python,https://github.com/ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python,2023-01-06 18:09:54,2023-08-11 16:49:35.000,2023-08-11 16:49:35,47.0,,5.0,6.0,13.0,2.0,,6.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -348,MolSLEPA,,generative,MIT,https://github.com/tsudalab/MolSLEPA,Interpretable Fragment-based Molecule Design with Self-learning Entropic Population Annealing.,5,False,['xai'],tsudalab/MolSLEPA,https://github.com/tsudalab/MolSLEPA,2023-04-10 15:04:55,2023-04-13 12:48:49.000,2023-04-13 12:48:49,11.0,,1.0,8.0,2.0,,,5.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -349,MXenes4HER,,rep-eng,GPL-3.0,https://github.com/cnislab/MXenes4HER,Predicting hydrogen evolution (HER) activity over 4500 MXene materials https://doi.org/10.1039/D3TA00344B.,5,False,"['materials-discovery', 'catalysis', 'scikit-learn', 'single-paper']",cnislab/MXenes4HER,https://github.com/cnislab/MXenes4HER,2022-11-28 09:27:36,2023-02-27 18:08:05.000,2023-02-27 18:08:05,67.0,,3.0,1.0,1.0,,,5.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -350,COSMO tools,,others,,https://github.com/lab-cosmo/cosmo-tools,"Scripts, jupyter nbs, and general helpful stuff from COSMO by COSMO.",5,False,,lab-cosmo/cosmo-tools,https://github.com/lab-cosmo/cosmo-tools,2018-11-06 09:40:00,2024-05-24 05:53:06.000,2024-05-24 05:53:06,63.0,1.0,4.0,23.0,,,,4.0,,,,4.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,True -351,q-pac,,ml-esm,MIT,https://gitlab.com/jmargraf/qpac,Kernel charge equilibration method.,5,False,['electrostatics'],,,2020-11-15 20:11:27,2020-11-15 20:11:27.000,,,,4.0,,,2.0,,4.0,,,0.0,,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,jmargraf/qpac,https://gitlab.com/jmargraf/qpac,, -352,paper-ml-robustness-material-property,,unsupervised,BSD-3-Clause,https://github.com/mathsphy/paper-ml-robustness-material-property,A critical examination of robustness and generalizability of machine learning prediction of materials properties.,5,False,"['datasets', 'single-paper']",mathsphy/paper-ml-robustness-material-property,https://github.com/mathsphy/paper-ml-robustness-material-property,2023-02-21 02:38:13,2023-04-13 01:18:02.000,2023-04-13 01:18:02,3.0,,3.0,1.0,,,,4.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -353,SISSO++,,rep-eng,Apache-2.0,https://gitlab.com/sissopp_developers/sissopp,C++ Implementation of SISSO with python bindings.,5,False,['lang-cpp'],,,2021-04-30 14:20:59,2021-04-30 14:20:59.000,,,,3.0,,,3.0,18.0,3.0,,,1.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,sissopp_developers/sissopp,https://gitlab.com/sissopp_developers/sissopp,, -354,Alchemical learning,,ml-iap,BSD-3-Clause,https://github.com/Luthaf/alchemical-learning,Code for the Modeling high-entropy transition metal alloys with alchemical compression article.,5,False,,Luthaf/alchemical-learning,https://github.com/Luthaf/alchemical-learning,2021-12-02 17:02:00,2023-04-24 18:35:45.000,2023-04-07 10:19:10,120.0,,1.0,7.0,1.0,,4.0,2.0,,,,10.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -355,Visual Graph Datasets,,datasets,MIT,https://github.com/aimat-lab/visual_graph_datasets,Datasets for the training of graph neural networks (GNNs) and subsequent visualization of attributional explanations..,5,False,"['xai', 'rep-learn']",aimat-lab/visual_graph_datasets,https://github.com/aimat-lab/visual_graph_datasets,2023-06-01 11:33:18,2024-06-26 14:06:17.000,2024-06-26 14:06:13,53.0,3.0,2.0,3.0,,1.0,,1.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -356,linear-regression-benchmarks,,datasets,MIT,https://github.com/BingqingCheng/linear-regression-benchmarks,Data sets used for linear regression benchmarks.,5,False,"['benchmarking', 'single-paper']",BingqingCheng/linear-regression-benchmarks,https://github.com/BingqingCheng/linear-regression-benchmarks,2020-04-16 20:48:28,2022-01-26 08:29:46.000,2022-01-26 08:29:46,24.0,,,3.0,2.0,,,1.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -357,"Data Handling, DoE and Statistical Analysis for Material Chemists",,educational,GPL-3.0,https://github.com/Teoroo-CMC/DoE_Course_Material,"Notebooks for workshops of DoE course, hosted by the Computational Materials Chemistry group at Uppsala University.",5,False,,Teoroo-CMC/DoE_Course_Material,https://github.com/Teoroo-CMC/DoE_Course_Material,2023-05-22 08:11:41,2023-06-26 12:48:17.000,2023-06-26 12:48:15,157.0,,15.0,2.0,1.0,,,1.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -358,Per-Site CGCNN,,rep-learn,MIT,https://github.com/learningmatter-mit/per-site_cgcnn,Crystal graph convolutional neural networks for predicting material properties.,5,False,"['pretrained', 'single-paper']",learningmatter-mit/per-site_cgcnn,https://github.com/learningmatter-mit/per-site_cgcnn,2023-05-30 18:59:03,2023-06-05 17:38:46.000,2023-06-05 17:38:41,28.0,,,,,,,1.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -359,Per-site PAiNN,,rep-learn,MIT,https://github.com/learningmatter-mit/per-site_painn,Fork of PaiNN for PerovskiteOrderingGCNNs.,5,False,"['probabilistic', 'pretrained', 'single-paper']",learningmatter-mit/per-site_painn,https://github.com/learningmatter-mit/per-site_painn,2023-06-04 14:23:49,2023-06-05 17:35:19.000,2023-06-05 17:30:34,123.0,,1.0,,,,,1.0,,,,10.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -360,Geometric-GNNs,,community,,https://github.com/AlexDuvalinho/geometric-gnns,List of Geometric GNNs for 3D atomic systems.,4,False,"['datasets', 'educational', 'rep-learn']",AlexDuvalinho/geometric-gnns,https://github.com/AlexDuvalinho/geometric-gnns,2023-08-31 09:10:32,2024-02-29 16:25:54.000,2024-02-29 16:25:53,37.0,,6.0,1.0,3.0,,1.0,87.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -361,ML-in-chemistry-101,,educational,,https://github.com/BingqingCheng/ML-in-chemistry-101,The course materials for Machine Learning in Chemistry 101.,4,False,,BingqingCheng/ML-in-chemistry-101,https://github.com/BingqingCheng/ML-in-chemistry-101,2020-02-09 17:47:07,2020-10-19 08:10:31.000,2020-10-19 08:10:30,13.0,,17.0,2.0,,,,67.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -362,MAGUS,,materials-discovery,,https://gitlab.com/bigd4/magus,Machine learning And Graph theory assisted Universal structure Searcher.,4,False,"['structure-prediction', 'active-learning']",,,2023-01-31 09:00:23,2023-01-31 09:00:23.000,,,,15.0,,,,,56.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,bigd4/magus,https://gitlab.com/bigd4/magus,, -363,Allegro-Legato,,ml-iap,MIT,https://github.com/ibayashi-hikaru/allegro-legato,An extension of Allegro with enhanced robustness and time-to-failure.,4,False,['md'],ibayashi-hikaru/allegro-legato,https://github.com/ibayashi-hikaru/allegro-legato,2023-01-17 19:46:10,2023-08-03 22:25:11.000,2023-08-03 22:24:35,82.0,,1.0,1.0,,,,19.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -364,glp,,ml-iap,MIT,https://github.com/sirmarcel/glp,tools for graph-based machine-learning potentials in jax.,4,False,,sirmarcel/glp,https://github.com/sirmarcel/glp,2023-03-27 15:19:40,2024-04-09 12:06:56.000,2024-03-20 09:00:27,11.0,,1.0,2.0,3.0,,,17.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -365,Graph Transport Network,,rep-learn,https://github.com/gasteigerjo/gtn/blob/main/LICENSE.md,https://github.com/gasteigerjo/gtn,"Graph transport network (GTN), as proposed in Scalable Optimal Transport in High Dimensions for Graph Distances,..",4,False,['transport-phenomena'],gasteigerjo/gtn,https://github.com/gasteigerjo/gtn,2021-07-11 23:36:22,2023-04-26 14:22:00.000,2023-04-26 14:22:00,9.0,,3.0,2.0,,,,16.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -366,ChemDataWriter,,language-models,MIT,https://github.com/ShuHuang/chemdatawriter,ChemDataWriter is a transformer-based library for automatically generating research books in the chemistry area.,4,False,['literature-data'],ShuHuang/chemdatawriter,https://github.com/ShuHuang/chemdatawriter,2023-09-22 10:05:25,2023-10-07 04:23:47.000,2023-10-07 04:07:59,9.0,,1.0,2.0,,1.0,,14.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -367,SPINNER,,materials-discovery,GPL-3.0,https://github.com/MDIL-SNU/SPINNER,SPINNER (Structure Prediction of Inorganic crystals using Neural Network potentials with Evolutionary and Random..,4,False,"['lang-cpp', 'structure-prediction']",MDIL-SNU/SPINNER,https://github.com/MDIL-SNU/SPINNER,2021-07-15 02:10:58,2024-07-20 05:12:50.000,2021-11-25 07:58:15,102.0,,2.0,1.0,,1.0,,12.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -368,charge-density-models,,ml-dft,MIT,https://github.com/ulissigroup/charge-density-models,Tools to build charge density models using [fairchem](https://github.com/FAIR-Chem/fairchem).,4,False,['rep-learn'],ulissigroup/charge-density-models,https://github.com/ulissigroup/charge-density-models,2022-06-22 13:47:53,2023-11-29 15:07:42.000,2023-11-29 15:07:42,96.0,,2.0,2.0,16.0,1.0,2.0,10.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -369,paper-data-redundancy,,datasets,BSD-3-Clause,https://github.com/mathsphy/paper-data-redundancy,Repo for the paper Exploiting redundancy in large materials datasets for efficient machine learning with less data.,4,False,"['small-data', 'single-paper']",mathsphy/paper-data-redundancy,https://github.com/mathsphy/paper-data-redundancy,2023-06-10 15:00:28,2024-03-22 20:24:35.000,2024-03-22 20:24:34,17.0,,,1.0,,,,7.0,2023-10-11 14:09:07.000,1.0,1.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -370,TensorPotential,,ml-iap,https://github.com/ICAMS/TensorPotential/blob/main/LICENSE.md,https://cortner.github.io/ACEweb/software/,"Tensorpotential is a TensorFlow based tool for development, fitting ML interatomic potentials from electronic..",4,False,,ICAMS/TensorPotential,https://github.com/ICAMS/TensorPotential,2021-12-08 12:10:04,2023-07-10 16:37:18.000,2023-07-10 16:37:18,18.0,,4.0,2.0,2.0,,,7.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -371,Mapping out phase diagrams with generative classifiers,,generative,MIT,https://github.com/arnoldjulian/Mapping-out-phase-diagrams-with-generative-classifiers,Repository for our ``Mapping out phase diagrams with generative models paper.,4,False,['phase-transition'],arnoldjulian/Mapping-out-phase-diagrams-with-generative-classifiers,https://github.com/arnoldjulian/Mapping-out-phase-diagrams-with-generative-classifiers,2023-06-07 21:43:14,2023-06-27 08:12:29.000,2023-06-27 08:12:29,39.0,,2.0,1.0,,,,7.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -372,chemrev-gpr,,educational,,https://github.com/gabor1/chemrev-gpr,Notebooks accompanying the paper on GPR in materials and molecules in Chemical Reviews 2020.,4,False,,gabor1/chemrev-gpr,https://github.com/gabor1/chemrev-gpr,2020-12-18 23:48:06,2021-05-04 19:21:34.000,2021-05-04 19:21:30,10.0,,6.0,4.0,,,,6.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -373,automl-materials,,rep-eng,MIT,https://github.com/mm-tud/automl-materials,AutoML for Regression Tasks on Small Tabular Data in Materials Design.,4,False,"['automl', 'benchmarking', 'single-paper']",mm-tud/automl-materials,https://github.com/mm-tud/automl-materials,2022-10-07 09:49:18,2022-11-15 15:22:54.000,2022-11-15 15:22:45,6.0,,1.0,2.0,,,,5.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -374,ML-atomate,,materials-discovery,GPL-3.0,https://github.com/takahashi-akira-36m/ml_atomate,Machine learning-assisted Atomate code for autonomous computational materials screening.,4,False,"['active-learning', 'workflows']",takahashi-akira-36m/ml_atomate,https://github.com/takahashi-akira-36m/ml_atomate,2023-09-21 08:45:10,2023-11-17 09:54:23.000,2023-11-17 09:51:02,6.0,,1.0,1.0,,,,4.0,2023-09-29 03:52:46.000,stam_m_2023_fix,2.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -375,gkx: Green-Kubo Method in JAX,,rep-learn,MIT,https://github.com/sirmarcel/gkx,Green-Kubo + JAX + MLPs = Anharmonic Thermal Conductivities Done Fast.,4,False,['transport-phenomena'],sirmarcel/gkx,https://github.com/sirmarcel/gkx,2023-04-30 12:25:16,2024-03-20 09:05:20.000,2024-03-20 09:05:14,3.0,,,1.0,,,,3.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -376,ACEatoms,,general-tool,https://github.com/ACEsuit/ACEatoms.jl/blob/main/ASL.md,https://github.com/ACEsuit/ACEatoms.jl,Generic code for modelling atomic properties using ACE.,4,False,['lang-julia'],ACEsuit/ACEatoms.jl,https://github.com/ACEsuit/ACEatoms.jl,2021-03-23 23:50:03,2023-01-13 21:35:06.000,2023-01-13 21:28:08,134.0,,1.0,3.0,14.0,4.0,3.0,2.0,,,,10.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -377,halex,,ml-esm,,https://github.com/ecignoni/halex,Hamiltonian Learning for Excited States https://doi.org/10.48550/arXiv.2311.00844.,4,False,['excited-states'],ecignoni/halex,https://github.com/ecignoni/halex,2023-09-04 06:54:15,2024-02-08 10:20:53.000,2024-02-08 10:20:49,169.0,,,3.0,,1.0,,2.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -378,AI4ChemMat Hands-On Series,,educational,MPL-2.0,https://github.com/ai4chemmat/ai4chemmat.github.io,Hands-On Series organized by Chemistry and Materials working group at Argonne Nat Lab.,4,False,,ai4chemmat/ai4chemmat.github.io,https://github.com/ai4chemmat/ai4chemmat.github.io,2023-03-24 21:25:21,2024-04-24 16:32:18.000,2024-04-24 16:32:18,40.0,,,2.0,,,,1.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -379,magnetism-prediction,,rep-eng,Apache-2.0,https://github.com/dppant/magnetism-prediction,DFT-aided Machine Learning Search for Magnetism in Fe-based Bimetallic Chalcogenides.,4,False,"['magnetism', 'single-paper']",dppant/magnetism-prediction,https://github.com/dppant/magnetism-prediction,2022-09-13 03:58:10,2023-07-19 13:25:49.000,2023-07-19 13:25:49,46.0,,,3.0,,,,1.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -380,ACE Workflows,,ml-iap,,https://github.com/ACEsuit/ACEworkflows,Workflow Examples for ACE Models.,4,False,"['lang-julia', 'workflows']",ACEsuit/ACEworkflows,https://github.com/ACEsuit/ACEworkflows,2023-04-04 16:57:36,2023-10-12 18:01:00.000,2023-10-12 18:00:39,45.0,,1.0,3.0,7.0,1.0,,,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -381,gprep,,ml-dft,MIT,https://gitlab.com/jmargraf/gprep,Fitting DFTB repulsive potentials with GPR.,4,False,['single-paper'],,,2019-09-30 09:15:04,2019-09-30 09:15:04.000,,,,0.0,,,,,,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,jmargraf/gprep,https://gitlab.com/jmargraf/gprep,, -382,closed-loop-acceleration-benchmarks,,materials-discovery,MIT,https://github.com/aced-differentiate/closed-loop-acceleration-benchmarks,Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational..,4,False,"['materials-discovery', 'active-learning', 'single-paper']",aced-differentiate/closed-loop-acceleration-benchmarks,https://github.com/aced-differentiate/closed-loop-acceleration-benchmarks,2022-11-10 20:22:30,2023-07-25 21:25:42.000,2023-05-02 17:07:48,17.0,,1.0,4.0,3.0,,,,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -383,PeriodicPotentials,,ml-iap,MIT,https://github.com/AaltoRSE/PeriodicPotentials,A Periodic table app that displays potentials based on the selected elements.,4,False,"['community', 'visualization', 'lang-js']",AaltoRSE/PeriodicPotentials,https://github.com/AaltoRSE/PeriodicPotentials,2022-10-14 09:03:59,2022-10-18 17:10:22.000,2022-10-18 17:10:22,17.0,,1.0,3.0,3.0,,,,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -384,CatBERTa,,language-models,,https://github.com/hoon-ock/CatBERTa,Large Language Model for Catalyst Property Prediction.,3,False,"['transformer', 'catalysis']",hoon-ock/CatBERTa,https://github.com/hoon-ock/CatBERTa,2023-05-19 18:23:17,2024-03-08 02:59:22.000,2024-03-08 02:59:22,93.0,,2.0,1.0,2.0,,,19.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -385,ALEBREW,,active-learning,https://github.com/nec-research/alebrew/blob/main/LICENSE.txt,https://github.com/nec-research/alebrew,Official repository for the paper Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic..,3,False,"['ml-iap', 'md']",nec-research/alebrew,https://github.com/nec-research/alebrew,2024-02-27 07:32:23,2024-03-17 13:51:57.000,2024-03-17 13:51:52,2.0,,3.0,1.0,,1.0,,9.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -386,atom_by_atom,,rep-learn,,https://github.com/learningmatter-mit/atom_by_atom,Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with Machine Learning.,3,False,"['surface-science', 'single-paper']",learningmatter-mit/atom_by_atom,https://github.com/learningmatter-mit/atom_by_atom,2023-05-30 20:18:00,2023-10-19 15:59:08.000,2023-10-19 15:35:49,74.0,,,2.0,,,,6.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -387,DeepCDP,,ml-dft,,https://github.com/siddarthachar/deepcdp,DeepCDP: Deep learning Charge Density Prediction.,3,False,,siddarthachar/deepcdp,https://github.com/siddarthachar/deepcdp,2021-12-18 14:26:56,2023-06-16 20:38:23.000,2023-06-16 20:38:23,96.0,,1.0,2.0,27.0,,,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -388,Element encoder,,rep-learn,GPL-3.0,https://github.com/jeherr/element-encoder,Autoencoder neural network to compress properties of atomic species into a vector representation.,3,False,['single-paper'],jeherr/element-encoder,https://github.com/jeherr/element-encoder,2019-03-27 17:11:30,2020-01-09 15:54:27.000,2020-01-09 15:54:26,8.0,,2.0,4.0,,,1.0,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -389,interface-lammps-mlip-3,,md,GPL-2.0,https://gitlab.com/ivannovikov/interface-lammps-mlip-3,An interface between LAMMPS and MLIP (version 3).,3,False,,,,2023-04-24 12:48:51,2023-04-24 12:48:51.000,,,,5.0,,,4.0,1.0,5.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,ivannovikov/interface-lammps-mlip-3,https://gitlab.com/ivannovikov/interface-lammps-mlip-3,, -390,sl_discovery,,materials-discovery,Apache-2.0,https://github.com/CitrineInformatics-ERD-public/sl_discovery,Data processing and models related to Quantifying the performance of machine learning models in materials discovery.,3,False,"['materials-discovery', 'single-paper']",CitrineInformatics-ERD-public/sl_discovery,https://github.com/CitrineInformatics-ERD-public/sl_discovery,2022-10-24 18:10:14,2022-12-20 23:46:05.000,2022-12-20 23:45:57,5.0,,2.0,2.0,,,,5.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -391,APET,,ml-dft,GPL-3.0,https://github.com/emotionor/APET,Atomic Positional Embedding-based Transformer.,3,False,"['density-of-states', 'transformer']",emotionor/APET,https://github.com/emotionor/APET,2023-03-06 01:53:16,2024-04-24 03:43:39.000,2023-09-28 03:16:11,11.0,,,2.0,,,,4.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -392,e3psi,,ml-esm,LGPL-3.0,https://github.com/muhrin/e3psi,Equivariant machine learning library for learning from electronic structures.,3,False,,muhrin/e3psi,https://github.com/muhrin/e3psi,2022-08-08 10:48:30,2024-01-05 12:59:56.000,2024-01-05 12:59:09,19.0,,,2.0,,,,3.0,,,,,,,1.0,1.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -393,torch_spex,,math,,https://github.com/lab-cosmo/torch_spex,Spherical expansions in PyTorch.,3,False,,lab-cosmo/torch_spex,https://github.com/lab-cosmo/torch_spex,2023-03-28 09:48:36,2024-03-28 05:33:01.000,2024-03-28 05:33:01,77.0,,2.0,18.0,35.0,7.0,2.0,3.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -394,PiNN Lab,,educational,GPL-3.0,https://github.com/Teoroo-CMC/PiNN_lab,Material for running a lab session on atomic neural networks.,3,False,,Teoroo-CMC/PiNN_lab,https://github.com/Teoroo-CMC/PiNN_lab,2019-03-17 22:09:30,2023-05-01 15:59:56.000,2023-05-01 15:59:22,9.0,,1.0,3.0,1.0,,,2.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -395,CSNN,,ml-dft,BSD-3-Clause,https://github.com/foxjas/CSNN,Primary codebase of CSNN - Concentric Spherical Neural Network for 3D Representation Learning.,3,False,,foxjas/CSNN,https://github.com/foxjas/CSNN,2022-05-19 15:40:49,2022-10-11 04:27:40.000,2022-10-11 04:27:40,6.0,,,1.0,,,,2.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -396,Linear vs blackbox,,xai,MIT,https://github.com/CitrineInformatics-ERD-public/linear-vs-blackbox,Code and data related to the publication: Interpretable models for extrapolation in scientific machine learning.,3,False,"['xai', 'single-paper', 'rep-eng']",CitrineInformatics-ERD-public/linear-vs-blackbox,https://github.com/CitrineInformatics-ERD-public/linear-vs-blackbox,2022-12-02 20:32:53,2022-12-16 18:48:12.000,2022-12-16 18:48:12,4.0,,,1.0,,,,2.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -397,MALADA,,ml-dft,BSD-3-Clause,https://github.com/mala-project/malada,MALA Data Acquisition: Helpful tools to build data for MALA.,3,False,,mala-project/malada,https://github.com/mala-project/malada,2021-07-26 05:46:08,2024-07-26 14:19:51.000,2023-05-24 09:18:24,111.0,,1.0,2.0,4.0,17.0,2.0,1.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -398,ML-for-CurieTemp-Predictions,,rep-eng,MIT,https://github.com/msg-byu/ML-for-CurieTemp-Predictions,Machine Learning Predictions of High-Curie-Temperature Materials.,3,False,"['single-paper', 'magnetism']",msg-byu/ML-for-CurieTemp-Predictions,https://github.com/msg-byu/ML-for-CurieTemp-Predictions,2023-06-05 22:46:47,2023-06-14 19:05:50.000,2023-06-14 19:05:47,25.0,,,1.0,,,,1.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -399,Magpie,,general-tool,MIT,https://bitbucket.org/wolverton/magpie/,Materials Agnostic Platform for Informatics and Exploration (Magpie).,3,False,['lang-java'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -400,PyFLAME,,ml-iap,,https://gitlab.com/flame-code/PyFLAME,An automated approach for developing neural network interatomic potentials with FLAME..,3,False,"['active-learning', 'structure-prediction', 'structure-optimization', 'rep-eng', 'lang-fortran']",,,2021-04-07 09:16:07,2021-04-07 09:16:07.000,,,,4.0,,,,,,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,flame-code/PyFLAME,https://gitlab.com/flame-code/PyFLAME,, -401,nep-data,,datasets,,https://gitlab.com/brucefan1983/nep-data,Data related to the NEP machine-learned potential of GPUMD.,2,False,"['ml-iap', 'md', 'transport-phenomena']",,,2021-11-22 19:43:01,2021-11-22 19:43:01.000,,,,4.0,,,1.0,1.0,12.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,brucefan1983/nep-data,https://gitlab.com/brucefan1983/nep-data,, -402,SingleNN,,ml-iap,,https://github.com/lmj1029123/SingleNN,An efficient package for training and executing neural-network interatomic potentials.,2,False,['lang-cpp'],lmj1029123/SingleNN,https://github.com/lmj1029123/SingleNN,2020-03-11 18:36:16,2021-11-09 00:40:18.000,2021-11-09 00:40:10,17.0,,1.0,1.0,,1.0,,8.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -403,A3MD,,ml-dft,,https://github.com/brunocuevas/a3md,MPNN-like + Analytic Density Model = Accurate electron densities.,2,False,"['rep-learn', 'single-paper']",brunocuevas/a3md,https://github.com/brunocuevas/a3md,2021-06-02 07:23:17,2021-12-02 17:10:39.000,2021-12-02 17:10:34,4.0,,1.0,2.0,,,,8.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -404,MLDensity_tutorial,,educational,,https://github.com/bfocassio/MLDensity_tutorial,Tutorial files to work with ML for the charge density in molecules and solids.,2,False,,bfocassio/MLDensity_tutorial,https://github.com/bfocassio/MLDensity_tutorial,2023-01-31 10:33:23,2023-02-22 19:20:32.000,2023-02-22 19:20:32,8.0,,1.0,1.0,,,,7.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -405,tmQM_wB97MV Dataset,,datasets,,https://github.com/ulissigroup/tmQM_wB97MV,Code for Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV..,2,False,"['catalysis', 'rep-learn']",ulissigroup/tmqm_wB97MV,https://github.com/ulissigroup/tmQM_wB97MV,2023-07-17 21:40:20,2024-04-09 22:01:26.000,2024-04-09 22:01:26,17.0,,1.0,3.0,,,2.0,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -406,LAMMPS-style pair potentials with GAP,,educational,,https://github.com/victorprincipe/pair_potentials,A tutorial on how to create LAMMPS-style pair potentials and use them in combination with GAP potentials to run MD..,2,False,"['ml-iap', 'md', 'rep-eng']",victorprincipe/pair_potentials,https://github.com/victorprincipe/pair_potentials,2022-09-21 09:45:03,2022-10-03 08:06:22.000,2022-10-03 08:05:53,36.0,,,1.0,1.0,,,4.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -407,KmdPlus,,unsupervised,,https://github.com/Minoru938/KmdPlus,"This module contains a class for treating kernel mean descriptor (KMD), and a function for generating descriptors with..",2,False,,Minoru938/KmdPlus,https://github.com/Minoru938/KmdPlus,2023-03-26 10:06:34,2023-10-17 08:28:01.000,2023-10-17 08:28:01,7.0,,1.0,1.0,,,,3.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -408,AisNet,,ml-iap,MIT,https://github.com/loilisxka/AisNet,A Universal Interatomic Potential Neural Network with Encoded Local Environment Features..,2,False,,loilisxka/AisNet,https://github.com/loilisxka/AisNet,2022-10-11 05:54:59,2022-10-11 06:02:47.000,2022-10-11 05:58:06,2.0,,,1.0,,,,3.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -409,MALA Tutorial,,educational,,https://github.com/mala-project/mala_tutorial,A full MALA hands-on tutorial.,2,False,,mala-project/mala_tutorial,https://github.com/mala-project/mala_tutorial,2023-03-09 14:01:54,2023-11-28 11:20:39.000,2023-11-28 11:17:01,24.0,,,2.0,,,,2.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -410,Wigner Kernels,,math,,https://github.com/lab-cosmo/wigner_kernels,Collection of programs to benchmark Wigner kernels.,2,False,['benchmarking'],lab-cosmo/wigner_kernels,https://github.com/lab-cosmo/wigner_kernels,2022-12-08 12:28:26,2023-07-08 15:48:41.000,2023-07-08 15:48:37,109.0,,,1.0,,,,2.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -411,quantum-structure-ml,,general-tool,,https://github.com/hgheiberger/quantum-structure-ml,Multi-class classification model for predicting the magnetic order of magnetic structures and a binary classification..,2,False,"['magnetism', 'benchmarking']",hgheiberger/quantum-structure-ml,https://github.com/hgheiberger/quantum-structure-ml,2020-10-05 01:11:01,2022-12-22 21:45:40.000,2022-12-22 21:45:40,19.0,,,2.0,,,,2.0,2022-08-18 05:25:24.000,1.0.0,1.0,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -412,RuNNer,,ml-iap,GPL-3.0,https://www.uni-goettingen.de/de/560580.html,The RuNNer Neural Network Energy Representation is a Fortran-based framework for the construction of Behler-..,2,False,['lang-fortran'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,https://theochemgoettingen.gitlab.io/RuNNer/, -413,Point Edge Transformer,,rep-learn,CC-BY-4.0,https://zenodo.org/record/7967079,"Smooth, exact rotational symmetrization for deep learning on point clouds.",2,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -414,nnp-pre-training,,ml-iap,,https://github.com/jla-gardner/nnp-pre-training,Synthetic pre-training for neural-network interatomic potentials.,1,False,"['pretrained', 'md']",jla-gardner/nnp-pre-training,https://github.com/jla-gardner/nnp-pre-training,2023-07-12 11:58:29,2023-12-19 12:08:14.000,2023-12-19 12:08:14,11.0,,,1.0,,,,6.0,2023-12-19 12:02:35.000,1.0,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -415,SphericalNet,,rep-learn,,https://github.com/risilab/SphericalNet,Implementation of Clebsch-Gordan Networks (CGnet: https://arxiv.org/pdf/1806.09231.pdf) by GElib & cnine libraries in..,1,False,,risilab/SphericalNet,https://github.com/risilab/SphericalNet,2022-05-31 14:39:05,2022-06-07 03:57:10.000,2022-06-07 03:53:49,1.0,,,2.0,,,,3.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -416,kdft,,ml-dft,,https://gitlab.com/jmargraf/kdf,The Kernel Density Functional (KDF) code allows generating ML based DFT functionals.,1,False,,,,2020-11-07 21:50:22,2020-11-07 21:50:22.000,,,,0.0,,,,,2.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,jmargraf/kdf,https://gitlab.com/jmargraf/kdf,, -417,mag-ace,,ml-iap,,https://github.com/mttrin93/mag-ace,Magnetic ACE potential. FORTRAN interface for LAMMPS SPIN package.,1,False,"['magnetism', 'md', 'lang-fortran']",mttrin93/mag-ace,https://github.com/mttrin93/mag-ace,2023-12-26 19:00:40,2023-12-26 22:34:27.000,2023-12-26 22:34:27,7.0,,,1.0,,,,2.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -418,mlp,,ml-iap,,https://github.com/cesmix-mit/MLP,Proper orthogonal descriptors for efficient and accurate interatomic potentials...,1,False,['lang-julia'],cesmix-mit/mlp,https://github.com/cesmix-mit/MLP,2022-02-25 23:03:09,2022-10-22 19:01:45.000,2022-10-22 19:01:42,12.0,,1.0,2.0,,,,1.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -419,GitHub topic materials-informatics,,community,,https://github.com/topics/materials-informatics,GitHub topic materials-informatics.,1,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -420,MateriApps,,community,,https://ma.issp.u-tokyo.ac.jp/en/,A Portal Site of Materials Science Simulation.,1,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, -421,Allegro-JAX,,ml-iap,,https://github.com/mariogeiger/allegro-jax,JAX implementation of the Allegro interatomic potential.,0,True,,mariogeiger/allegro-jax,https://github.com/mariogeiger/allegro-jax,2023-07-02 19:00:00,2024-04-09 18:44:30.000,2024-04-09 18:44:30,7.0,,2.0,2.0,1.0,1.0,1.0,17.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -422,Descriptor Embedding and Clustering for Atomisitic-environment Framework (DECAF),,unsupervised,,https://gitlab.mpcdf.mpg.de/klai/decaf,Provides a workflow to obtain clustering of local environments in dataset of structures.,0,False,,,,,,,41.0,,,,,,,2.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -423,MLDensity,,ml-dft,,https://github.com/StefanoSanvitoGroup/MLdensity,Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure..,0,False,,StefanoSanvitoGroup/MLdensity,https://github.com/StefanoSanvitoGroup/MLdensity,2023-01-31 20:44:45,2024-05-27 12:28:57.000,2023-02-22 19:25:51,14.0,,,2.0,,,,2.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +,name,resource,category,license,homepage,description,projectrank,show,labels,github_id,github_url,created_at,updated_at,last_commit_pushed_at,commit_count,recent_commit_count,fork_count,watchers_count,pr_count,open_issue_count,closed_issue_count,star_count,latest_stable_release_published_at,latest_stable_release_number,release_count,contributor_count,pypi_id,conda_id,dependent_project_count,github_dependent_project_count,pypi_url,pypi_latest_release_published_at,pypi_dependent_project_count,pypi_monthly_downloads,monthly_downloads,conda_url,conda_latest_release_published_at,conda_total_downloads,projectrank_placing,dockerhub_id,dockerhub_url,dockerhub_latest_release_published_at,dockerhub_stars,dockerhub_pulls,github_release_downloads,updated_github_id,maven_id,maven_url,maven_latest_release_published_at,maven_dependent_project_count,npm_id,npm_url,npm_latest_release_published_at,npm_dependent_project_count,npm_monthly_downloads,gitlab_id,gitlab_url,docs_url,ignore +0,AI for Science Map,True,community,GPL-3.0 license,https://www.air4.science/map,"Interactive mindmap of the AI4Science research field, including atomistic machine learning, including papers,..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1,Atomic Cluster Expansion,True,community,,https://cortner.github.io/ACEweb/,Atomic Cluster Expansion (ACE) community homepage.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2,CrystaLLM,True,community,https://materialis.ai/terms.html,https://crystallm.com,Generate a crystal structure from a composition.,0,True,"['language-models', 'generative', 'pretrained', 'transformer']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +3,GAP-ML.org community homepage,True,community,,https://gap-ml.org/,,0,True,['ml-iap'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +4,matsci.org,True,community,,https://matsci.org/,"A community forum for the discussion of anything materials science, with a focus on computational materials science..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +5,Matter Modeling Stack Exchange - Machine Learning,True,community,,https://mattermodeling.stackexchange.com/questions/tagged/machine-learning,"Forum StackExchange, site Matter Modeling, ML-tagged questions.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +6,Alexandria Materials Database,True,datasets,CC-BY-4.0,https://alexandria.icams.rub.de/,"A database of millions of theoretical crystal structures (3D, 2D and 1D) discovered by machine learning accelerated..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +7,Catalysis Hub,True,datasets,,https://www.catalysis-hub.org/,A web-platform for sharing data and software for computational catalysis research!.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +8,Citrination Datasets,True,datasets,MIT,https://citrination.com/,AI-Powered Materials Data Platform. Open Citrination has been decommissioned.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +9,crystals.ai,True,datasets,,https://crystals.ai/,Curated datasets for reproducible AI in materials science.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +10,DeepChem Models,True,datasets,,https://huggingface.co/DeepChem,DeepChem models on HuggingFace.,0,True,"['model-repository', 'pretrained', 'language-models']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +11,Graphs of Materials Project 20190401,True,datasets,MIT,https://figshare.com/articles/dataset/Graphs_of_Materials_Project_20190401/8097992,The dataset used to train the MEGNet interatomic potential.,0,True,['ml-iap'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +12,HME21 Dataset,True,datasets,,https://doi.org/10.6084/m9.figshare.19658538,High-temperature multi-element 2021 dataset for the PreFerred Potential (PFP)..,0,True,['uip'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +13,JARVIS-Leaderboard,True,datasets,https://github.com/usnistgov/jarvis_leaderboard/blob/main/LICENSE.rst,https://pages.nist.gov/jarvis_leaderboard/,Explore State-of-the-Art Materials Design Methods: https://www.nature.com/articles/s41524-024-01259-w.,0,True,"['model-repository', 'benchmarking', 'community', 'educational']",usnistgov/jarvis_leaderboard,https://github.com/usnistgov/jarvis_leaderboard,2022-07-15 16:48:33,2024-08-14 17:30:42.000,2024-08-14 04:18:28,823.0,11.0,42.0,5.0,320.0,18.0,2.0,56.0,2024-05-16 16:20:41.000,2024.4.26,28.0,33.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +14,Materials Project - Charge Densities,True,datasets,,https://materialsproject.org/ml/charge_densities,Materials Project has started offering charge density information available for download via their public API.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +15,Materials Project Trajectory (MPtrj) Dataset,True,datasets,MIT,https://figshare.com/articles/dataset/Materials_Project_Trjectory_MPtrj_Dataset/23713842,The dataset used to train the CHGNet universal potential.,0,True,['uip'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +16,matterverse.ai,True,datasets,,https://matterverse.ai/,Database of yet-to-be-sythesized materials predicted using state-of-the-art machine learning algorithms.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +17,MPF.2021.2.8,True,datasets,,https://figshare.com/articles/dataset/MPF_2021_2_8/19470599,The dataset used to train the M3GNet universal potential.,0,True,['uip'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +18,NRELMatDB,True,datasets,,https://materials.nrel.gov/,"Computational materials database with the specific focus on materials for renewable energy applications including, but..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +19,Quantum-Machine.org Datasets,True,datasets,,http://quantum-machine.org/datasets/,"Collection of datasets, including QM7, QM9, etc. MD, DFT. Small organic molecules, mostly.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +20,sGDML Datasets,True,datasets,,http://sgdml.org/#datasets,"MD17, MD22, DFT datasets.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +21,MoleculeNet,True,datasets,,https://moleculenet.org/,A Benchmark for Molecular Machine Learning.,0,True,['benchmarking'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +22,ZINC15,True,datasets,,https://zinc15.docking.org/,A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable..,0,True,"['graph', 'biomolecules']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +23,ZINC20,True,datasets,,https://zinc.docking.org/,A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable..,0,True,"['graph', 'biomolecules']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +24,AI for Science 101,True,educational,,https://ai4science101.github.io/,,0,True,"['community', 'rep-learn']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +25,AL4MS 2023 workshop tutorials,True,educational,,https://sites.utu.fi/al4ms2023/media-and-tutorials/,,0,True,['active-learning'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +26,Quantum Chemistry in the Age of Machine Learning,True,educational,,https://www.elsevier.com/books-and-journals/book-companion/9780323900492,"Book, 2022.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +27,MatterGen,True,materials-discovery,,https://www.microsoft.com/en-us/research/blog/mattergen-property-guided-materials-design/,A generative model for inorganic materials design https://doi.org/10.48550/arXiv.2312.03687.,0,True,"['generative', 'proprietary']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +28,IKS-PIML,True,ml-dft,,https://rodare.hzdr.de/record/2720,Code and generated data for the paper Inverting the Kohn-Sham equations with physics-informed machine learning..,0,True,"['neural-operator', 'pinn', 'datasets', 'single-paper']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +29,TeaNet,True,uip,,https://doi.org/10.24433/CO.0749085.v1,Universal neural network interatomic potential inspired by iterative electronic relaxations..,0,True,['ml-iap'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +30,PreFerred Potential (PFP),True,uip,,https://www.nature.com/articles/s41467-022-30687-9#code-availability,Universal neural network potential for material discovery https://doi.org/10.1038/s41467-022-30687-9.,0,True,"['ml-iap', 'proprietary']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +31,MatterSim,True,uip,,https://www.microsoft.com/en-us/research/blog/mattersim-a-deep-learning-model-for-materials-under-real-world-conditions/,"A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures https://doi.org/10.48550/arXiv.2405.04967.",0,True,"['ml-iap', 'active-learning', 'proprietary']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +32,Deep Graph Library (DGL),,rep-learn,Apache-2.0,https://github.com/dmlc/dgl,"Python package built to ease deep learning on graph, on top of existing DL frameworks.",39,True,,dmlc/dgl,https://github.com/dmlc/dgl,2018-04-20 14:49:09,2024-08-19 10:31:37.000,2024-08-18 23:21:54,4341.0,231.0,2963.0,172.0,4964.0,512.0,2340.0,13305.0,2024-06-28 00:16:49.000,2.3.0,453.0,295.0,dgl,dglteam/dgl,423.0,283.0,https://pypi.org/project/dgl,2024-05-13 01:10:39.000,140.0,118536.0,123912.0,https://anaconda.org/dglteam/dgl,2024-06-28 00:12:10.120,365602.0,1.0,,,,,,,,,,,,,,,,,,,, +33,DeepChem,,general-tool,MIT,https://github.com/deepchem/deepchem,"Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology.",36,True,,deepchem/deepchem,https://github.com/deepchem/deepchem,2015-09-24 23:20:28,2024-08-17 01:23:20.000,2024-08-16 16:15:00,10520.0,54.0,1650.0,145.0,2404.0,627.0,1236.0,5356.0,2024-04-03 16:21:23.000,2.8.0,909.0,247.0,deepchem,conda-forge/deepchem,431.0,418.0,https://pypi.org/project/deepchem,2024-08-16 16:21:57.000,13.0,44761.0,47003.0,https://anaconda.org/conda-forge/deepchem,2024-04-05 16:46:45.105,108654.0,1.0,deepchemio/deepchem,https://hub.docker.com/r/deepchemio/deepchem,2024-08-16 16:36:22.904668,5.0,7433.0,,,,,,,,,,,,,,, +34,RDKit,,general-tool,BSD-3-Clause,https://github.com/rdkit/rdkit,,36,True,['lang-cpp'],rdkit/rdkit,https://github.com/rdkit/rdkit,2013-05-12 06:19:15,2024-08-19 12:22:43.000,2024-08-16 15:11:31,7815.0,79.0,830.0,82.0,3192.0,932.0,2291.0,2567.0,2024-08-07 12:34:25.000,2024.3.5,100.0,228.0,rdkit,rdkit/rdkit,671.0,3.0,https://pypi.org/project/rdkit,2024-08-07 12:34:25.000,668.0,892141.0,913214.0,https://anaconda.org/rdkit/rdkit,2023-06-16 12:54:07.547,2569689.0,1.0,,,,,,1247.0,,,,,,,,,,,,,, +35,PyG Models,,rep-learn,MIT,https://github.com/pyg-team/pytorch_geometric/tree/master/torch_geometric/nn/models,Representation learning models implemented in PyTorch Geometric.,35,True,['general-ml'],pyg-team/pytorch_geometric,https://github.com/pyg-team/pytorch_geometric,2017-10-06 16:03:03,2024-08-19 12:51:52.000,2024-08-19 10:05:32,7599.0,78.0,3594.0,252.0,3093.0,1012.0,2645.0,20805.0,2024-04-19 11:37:44.000,2.5.3,40.0,513.0,,,6285.0,6285.0,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,, +36,e3nn,,rep-learn,MIT,https://github.com/e3nn/e3nn,A modular framework for neural networks with Euclidean symmetry.,28,True,,e3nn/e3nn,https://github.com/e3nn/e3nn,2020-01-31 13:06:42,2024-08-18 17:48:32.000,2024-07-26 23:00:39,2173.0,12.0,138.0,19.0,222.0,23.0,133.0,925.0,2024-07-27 03:01:58.000,0.5.2,29.0,31.0,e3nn,conda-forge/e3nn,285.0,281.0,https://pypi.org/project/e3nn,2022-04-13 19:24:30.000,4.0,241601.0,242350.0,https://anaconda.org/conda-forge/e3nn,2023-06-18 08:41:30.723,20241.0,1.0,,,,,,,,,,,,,,,,,,,, +37,paper-qa,,language-models,Apache-2.0,https://github.com/Future-House/paper-qa,LLM Chain for answering questions from documents with citations.,27,True,['ai-agent'],whitead/paper-qa,https://github.com/Future-House/paper-qa,2023-02-05 01:07:25,2024-08-14 21:02:09.000,2024-08-14 21:02:03,249.0,17.0,359.0,40.0,149.0,80.0,74.0,3827.0,2024-06-28 07:43:14.000,4.9.0,103.0,18.0,paper-qa,,73.0,65.0,https://pypi.org/project/paper-qa,2024-06-28 07:43:14.000,8.0,8343.0,8343.0,,,,1.0,,,,,,,Future-House/paper-qa,,,,,,,,,,,,, +38,DeePMD-kit,,ml-iap,LGPL-3.0,https://github.com/deepmodeling/deepmd-kit,A deep learning package for many-body potential energy representation and molecular dynamics.,27,True,['lang-cpp'],deepmodeling/deepmd-kit,https://github.com/deepmodeling/deepmd-kit,2017-12-12 15:23:44,2024-08-18 00:13:12.000,2024-07-06 15:29:41,2533.0,32.0,488.0,47.0,1985.0,84.0,667.0,1430.0,2024-07-03 19:29:34.000,2.2.11,53.0,69.0,deepmd-kit,deepmodeling/deepmd-kit,20.0,16.0,https://pypi.org/project/deepmd-kit,2024-07-27 04:40:19.000,4.0,1539.0,2266.0,https://anaconda.org/deepmodeling/deepmd-kit,2024-04-06 21:22:08.456,1218.0,1.0,deepmodeling/deepmd-kit,https://hub.docker.com/r/deepmodeling/deepmd-kit,2024-07-27 08:24:51.741318,1.0,2715.0,38369.0,,,,,,,,,,,,,, +39,Matminer,,general-tool,https://github.com/hackingmaterials/matminer/blob/main/LICENSE,https://github.com/hackingmaterials/matminer,Data mining for materials science.,27,True,,hackingmaterials/matminer,https://github.com/hackingmaterials/matminer,2015-09-24 20:37:00,2024-08-19 08:05:56.000,2024-07-30 12:11:47,4157.0,2.0,184.0,29.0,721.0,29.0,197.0,458.0,2024-03-27 14:48:51.000,0.9.2,71.0,54.0,matminer,conda-forge/matminer,363.0,305.0,https://pypi.org/project/matminer,2024-03-27 14:48:51.000,58.0,10605.0,12063.0,https://anaconda.org/conda-forge/matminer,2024-03-28 11:24:38.014,67109.0,1.0,,,,,,,,,,,,,,,,,,,, +40,DPA-2,,uip,LGPL-3.0,https://github.com/deepmodeling/deepmd-kit,Towards a universal large atomic model for molecular and material simulation https://doi.org/10.48550/arXiv.2312.15492.,26,True,"['ml-iap', 'pretrained', 'workflows', 'datasets']",deepmodeling/deepmd-kit,https://github.com/deepmodeling/deepmd-kit,2017-12-12 15:23:44,2024-08-18 00:13:12.000,2024-07-06 15:29:41,2533.0,32.0,488.0,47.0,1985.0,84.0,667.0,1429.0,2024-07-03 19:22:15.000,2.2.11,49.0,69.0,,,16.0,16.0,,,,,673.0,,,,1.0,,,,,,38369.0,,,,,,,,,,,,,, +41,JAX-MD,,md,Apache-2.0,https://github.com/jax-md/jax-md,"Differentiable, Hardware Accelerated, Molecular Dynamics.",26,True,,jax-md/jax-md,https://github.com/jax-md/jax-md,2019-05-13 21:03:37,2024-08-02 20:06:09.000,2024-08-02 20:06:09,920.0,30.0,182.0,48.0,169.0,69.0,79.0,1145.0,2023-08-09 23:18:24.000,0.2.8,38.0,33.0,jax-md,,56.0,53.0,https://pypi.org/project/jax-md,2023-08-09 23:18:24.000,3.0,3171.0,3171.0,,,,1.0,,,,,,,,,,,,,,,,,,,, +42,SchNetPack,,rep-learn,MIT,https://github.com/atomistic-machine-learning/schnetpack,SchNetPack - Deep Neural Networks for Atomistic Systems.,26,True,,atomistic-machine-learning/schnetpack,https://github.com/atomistic-machine-learning/schnetpack,2018-09-03 15:44:35,2024-08-16 12:43:58.000,2024-08-15 11:53:41,1679.0,23.0,209.0,32.0,410.0,5.0,242.0,760.0,2023-09-29 14:32:16.000,2.0.4,10.0,35.0,schnetpack,,88.0,84.0,https://pypi.org/project/schnetpack,2023-09-29 14:32:16.000,4.0,751.0,751.0,,,,1.0,,,,,,,,,,,,,,,,,,,, +43,cdk,,rep-eng,LGPL-2.1,https://github.com/cdk/cdk,The Chemistry Development Kit.,26,True,"['cheminformatics', 'lang-java']",cdk/cdk,https://github.com/cdk/cdk,2010-05-11 08:30:07,2024-08-09 05:46:33.000,2024-08-07 15:06:18,17638.0,51.0,154.0,40.0,813.0,32.0,255.0,485.0,2023-08-21 19:50:47.000,cdk-2.9,20.0,165.0,,,16.0,,,,,,178.0,,,,1.0,,,,,,21274.0,,org.openscience.cdk:cdk-bundle,https://search.maven.org/artifact/org.openscience.cdk/cdk-bundle,2023-08-21 08:05:58,16.0,,,,,,,,, +44,QUIP,,general-tool,GPL-2.0,https://github.com/libAtoms/QUIP,libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io.,26,True,"['md', 'ml-iap', 'rep-eng', 'lang-fortran']",libAtoms/QUIP,https://github.com/libAtoms/QUIP,2013-07-02 15:21:59,2024-08-17 08:38:24.000,2024-08-15 08:10:51,10864.0,25.0,124.0,26.0,177.0,103.0,358.0,350.0,2023-06-15 19:54:01.129,0.9.14,15.0,85.0,quippy-ase,,45.0,41.0,https://pypi.org/project/quippy-ase,2023-01-15 16:54:03.041,4.0,4160.0,4242.0,,,,2.0,libatomsquip/quip,https://hub.docker.com/r/libatomsquip/quip,2023-04-24 21:25:17.345957,4.0,9905.0,533.0,,,,,,,,,,,,,, +45,OPTIMADE Python tools,,datasets,MIT,https://github.com/Materials-Consortia/optimade-python-tools,Tools for implementing and consuming OPTIMADE APIs in Python.,26,True,,Materials-Consortia/optimade-python-tools,https://github.com/Materials-Consortia/optimade-python-tools,2018-06-05 21:00:07,2024-08-19 07:43:47.000,2024-08-19 07:43:46,1650.0,43.0,42.0,7.0,1695.0,102.0,348.0,64.0,2024-07-20 12:57:23.000,1.1.1,107.0,28.0,optimade,conda-forge/optimade,52.0,48.0,https://pypi.org/project/optimade,2024-07-20 12:57:23.000,4.0,5421.0,7296.0,https://anaconda.org/conda-forge/optimade,2024-07-20 16:43:01.881,84379.0,1.0,,,,,,,,,,,,,,,,,,,, +46,JAX-DFT,,ml-dft,Apache-2.0,https://github.com/google-research/google-research/tree/master/jax_dft,This library provides basic building blocks that can construct DFT calculations as a differentiable program.,25,True,,google-research/google-research,https://github.com/google-research/google-research,2018-10-04 18:42:48,2024-08-16 18:10:42.000,2024-08-16 18:10:35,4622.0,107.0,7793.0,748.0,860.0,1422.0,325.0,33644.0,,,,796.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,, +47,DScribe,,rep-eng,Apache-2.0,https://github.com/SINGROUP/dscribe,DScribe is a python package for creating machine learning descriptors for atomistic systems.,25,True,,SINGROUP/dscribe,https://github.com/SINGROUP/dscribe,2017-05-08 08:29:51,2024-08-13 20:50:00.000,2024-05-28 18:24:28,1288.0,1.0,86.0,20.0,27.0,12.0,92.0,388.0,2024-05-28 18:22:25.000,2.1.1,32.0,18.0,dscribe,conda-forge/dscribe,228.0,193.0,https://pypi.org/project/dscribe,2024-05-28 18:22:25.000,35.0,127567.0,130023.0,https://anaconda.org/conda-forge/dscribe,2024-05-28 23:16:49.298,120370.0,1.0,,,,,,,,,,,,,,,,,,,, +48,MAML,,general-tool,BSD-3-Clause,https://github.com/materialsvirtuallab/maml,"Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.",25,True,,materialsvirtuallab/maml,https://github.com/materialsvirtuallab/maml,2020-01-25 15:04:21,2024-08-19 13:06:11.000,2024-07-03 18:08:37,1747.0,48.0,75.0,21.0,581.0,9.0,61.0,346.0,2024-06-13 15:29:41.000,2024.6.13,16.0,32.0,maml,,12.0,10.0,https://pypi.org/project/maml,2024-06-13 15:29:41.000,2.0,192.0,192.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +49,NequIP,,ml-iap,MIT,https://github.com/mir-group/nequip,NequIP is a code for building E(3)-equivariant interatomic potentials.,24,True,,mir-group/nequip,https://github.com/mir-group/nequip,2021-03-15 23:44:39,2024-07-25 22:18:07.000,2024-07-09 15:58:45,1873.0,32.0,126.0,23.0,161.0,22.0,66.0,581.0,2024-07-09 16:05:06.000,0.6.1,16.0,11.0,nequip,conda-forge/nequip,24.0,23.0,https://pypi.org/project/nequip,2024-07-09 16:05:26.000,1.0,2571.0,2768.0,https://anaconda.org/conda-forge/nequip,2024-07-10 05:13:00.157,5335.0,1.0,,,,,,,,,,,,,,,,,,,, +50,dgl-lifesci,,rep-learn,Apache-2.0,https://github.com/awslabs/dgl-lifesci,Python package for graph neural networks in chemistry and biology.,23,False,,awslabs/dgl-lifesci,https://github.com/awslabs/dgl-lifesci,2020-04-23 07:14:21,2023-11-01 19:32:07.000,2023-04-16 03:55:52,236.0,,140.0,17.0,141.0,26.0,57.0,704.0,2023-02-13 08:45:17.000,0.3.2,17.0,22.0,dgllife,,229.0,225.0,https://pypi.org/project/dgllife,2022-12-21 13:18:00.570,4.0,33952.0,33952.0,,,,1.0,,,,,,,,,,,,,,,,,,,, +51,TorchANI,,ml-iap,MIT,https://github.com/aiqm/torchani,Accurate Neural Network Potential on PyTorch.,23,True,,aiqm/torchani,https://github.com/aiqm/torchani,2018-04-02 15:43:04,2024-07-19 17:54:52.000,2023-11-14 16:32:59,434.0,,124.0,31.0,483.0,24.0,144.0,455.0,2023-11-14 16:41:14.000,2.2.4,24.0,19.0,torchani,conda-forge/torchani,43.0,39.0,https://pypi.org/project/torchani,2023-11-14 16:41:14.000,4.0,3596.0,12162.0,https://anaconda.org/conda-forge/torchani,2024-05-31 06:36:10.067,411207.0,1.0,,,,,,,,,,,,,,,,,,,, +52,TorchMD-NET,,ml-iap,MIT,https://github.com/torchmd/torchmd-net,Training neural network potentials.,23,True,"['md', 'rep-learn', 'transformer', 'pretrained']",torchmd/torchmd-net,https://github.com/torchmd/torchmd-net,2021-04-09 16:16:32,2024-08-16 08:57:15.000,2024-08-16 08:56:00,1286.0,70.0,69.0,11.0,229.0,34.0,84.0,309.0,2024-08-16 08:57:15.000,2.4.0,26.0,16.0,,conda-forge/torchmd-net,,,,,,,10849.0,https://anaconda.org/conda-forge/torchmd-net,2024-07-09 18:53:56.674,108499.0,1.0,,,,,,,,,,,,,,,,,,,, +53,JARVIS-Tools,,general-tool,https://github.com/usnistgov/jarvis/blob/master/LICENSE.rst,https://github.com/usnistgov/jarvis,JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications:..,23,True,,usnistgov/jarvis,https://github.com/usnistgov/jarvis,2017-06-22 19:34:02,2024-08-04 13:38:34.000,2024-06-28 03:35:23,2106.0,1.0,120.0,26.0,235.0,46.0,45.0,294.0,2024-06-28 03:39:12.000,2024.5.10,108.0,15.0,jarvis-tools,conda-forge/jarvis-tools,124.0,93.0,https://pypi.org/project/jarvis-tools,2024-06-28 03:39:12.000,31.0,21399.0,22998.0,https://anaconda.org/conda-forge/jarvis-tools,2024-06-28 03:50:37.135,73568.0,2.0,,,,,,,,,,,,,,,,,,,, +54,MatGL (Materials Graph Library),,rep-learn,BSD-3-Clause,https://github.com/materialsvirtuallab/matgl,Graph deep learning library for materials.,23,True,['multifidelity'],materialsvirtuallab/matgl,https://github.com/materialsvirtuallab/matgl,2022-08-29 18:36:05,2024-08-17 15:40:17.000,2024-08-17 15:40:16,1029.0,66.0,58.0,12.0,219.0,7.0,85.0,248.0,2024-08-07 12:24:58.000,1.1.3,31.0,17.0,m3gnet,,49.0,44.0,https://pypi.org/project/m3gnet,2022-11-17 23:25:34.805,5.0,626.0,626.0,,,,1.0,,,,,,,,,,,,,,,,,,,, +55,CHGNet,,uip,https://github.com/CederGroupHub/chgnet/blob/main/LICENSE,https://github.com/CederGroupHub/chgnet,Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov.,23,True,"['ml-iap', 'md', 'pretrained', 'electrostatics', 'magnetism', 'structure-relaxation']",CederGroupHub/chgnet,https://github.com/CederGroupHub/chgnet,2023-02-24 23:44:24,2024-08-18 19:19:11.000,2024-08-18 19:19:11,414.0,20.0,59.0,5.0,91.0,4.0,48.0,219.0,2024-06-05 16:47:28.000,0.3.8,16.0,7.0,chgnet,,44.0,30.0,https://pypi.org/project/chgnet,2024-06-05 16:47:28.000,14.0,28245.0,28245.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +56,dpdata,,data-structures,LGPL-3.0,https://github.com/deepmodeling/dpdata,"Manipulating multiple atomic simulation data formats, including DeePMD-kit, VASP, LAMMPS, ABACUS, etc.",23,True,,deepmodeling/dpdata,https://github.com/deepmodeling/dpdata,2019-04-12 13:24:23,2024-08-13 00:50:09.000,2024-06-06 00:03:38,724.0,6.0,128.0,9.0,477.0,28.0,80.0,195.0,2024-06-06 00:07:03.000,0.2.19,46.0,60.0,dpdata,deepmodeling/dpdata,162.0,122.0,https://pypi.org/project/dpdata,2024-06-06 00:07:03.000,40.0,26057.0,26063.0,https://anaconda.org/deepmodeling/dpdata,2023-09-27 20:07:36.945,218.0,1.0,,,,,,,,,,,,,,,,,,,, +57,MEGNet,,ml-iap,BSD-3-Clause,https://github.com/materialsvirtuallab/megnet,Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals.,22,False,['multifidelity'],materialsvirtuallab/megnet,https://github.com/materialsvirtuallab/megnet,2018-12-12 21:31:28,2023-04-27 02:39:17.000,2023-04-27 02:39:17,1146.0,,152.0,25.0,314.0,21.0,57.0,494.0,2022-11-16 21:25:01.818,1.3.2,37.0,14.0,megnet,,84.0,80.0,https://pypi.org/project/megnet,2022-11-16 21:25:01.818,4.0,429.0,429.0,,,,1.0,,,,,,,,,,,,,,,,,,,, +58,MACE,,ml-iap,MIT,https://github.com/ACEsuit/mace,MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.,22,True,,ACEsuit/mace,https://github.com/ACEsuit/mace,2022-06-21 18:44:34,2024-08-18 21:06:02.000,2024-08-12 10:01:02,699.0,58.0,169.0,24.0,221.0,54.0,168.0,449.0,2024-07-16 10:55:30.000,0.3.6,7.0,35.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,, +59,GPUMD,,ml-iap,GPL-3.0,https://github.com/brucefan1983/GPUMD,GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials..,22,True,"['md', 'lang-cpp', 'electrostatics']",brucefan1983/GPUMD,https://github.com/brucefan1983/GPUMD,2017-07-14 15:32:56,2024-08-19 05:55:47.000,2024-08-19 05:55:47,3962.0,233.0,112.0,27.0,521.0,23.0,161.0,429.0,2024-08-18 13:16:02.000,3.9.5,42.0,33.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,, +60,DP-GEN,,ml-iap,LGPL-3.0,https://github.com/deepmodeling/dpgen,The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field.,22,True,['workflows'],deepmodeling/dpgen,https://github.com/deepmodeling/dpgen,2019-06-13 11:43:56,2024-08-13 00:50:19.000,2024-04-10 06:31:36,2083.0,,172.0,13.0,835.0,35.0,261.0,291.0,2024-04-10 06:37:54.000,0.12.1,18.0,64.0,dpgen,deepmodeling/dpgen,7.0,6.0,https://pypi.org/project/dpgen,2024-04-10 06:37:54.000,1.0,355.0,390.0,https://anaconda.org/deepmodeling/dpgen,2023-06-16 19:27:03.566,204.0,1.0,,,,,,1745.0,,,,,,,,,,,,,, +61,pymatviz,,visualization,MIT,https://github.com/janosh/pymatviz,A toolkit for visualizations in materials informatics.,22,True,"['general-tool', 'probabilistic']",janosh/pymatviz,https://github.com/janosh/pymatviz,2021-02-21 12:40:34,2024-08-18 06:48:33.000,2024-08-18 06:44:53,327.0,51.0,13.0,7.0,151.0,9.0,32.0,151.0,2024-08-18 06:48:33.000,0.10.1,27.0,7.0,pymatviz,,10.0,8.0,https://pypi.org/project/pymatviz,2024-08-18 06:48:33.000,2.0,2122.0,2122.0,,,,1.0,,,,,,,,,,,,,,,,,,,, +62,MPContribs,,datasets,MIT,https://github.com/materialsproject/MPContribs,Platform for materials scientists to contribute and disseminate their materials data through Materials Project.,22,True,,materialsproject/MPContribs,https://github.com/materialsproject/MPContribs,2014-12-11 18:25:27,2024-08-19 08:09:58.000,2024-08-12 17:10:17,5576.0,40.0,20.0,10.0,1710.0,21.0,78.0,34.0,2024-06-20 22:37:55.000,5.8.4,162.0,25.0,mpcontribs-client,,41.0,38.0,https://pypi.org/project/mpcontribs-client,2024-06-20 22:37:55.000,3.0,1453.0,1453.0,,,,1.0,,,,,,,,,,,,,,,,,,,, +63,Best-of Machine Learning with Python,,community,CC-BY-4.0,https://github.com/ml-tooling/best-of-ml-python,A ranked list of awesome machine learning Python libraries. Updated weekly.,21,True,"['general-ml', 'lang-py']",ml-tooling/best-of-ml-python,https://github.com/ml-tooling/best-of-ml-python,2020-11-29 19:41:36,2024-07-22 15:36:22.000,2024-07-22 15:36:22,496.0,9.0,2336.0,409.0,266.0,25.0,34.0,16176.0,2024-06-06 15:57:10.000,2024.06.06,100.0,46.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,, +64,NVIDIA Deep Learning Examples for Tensor Cores,,rep-learn,https://github.com/NVIDIA/DeepLearningExamples/blob/master/DGLPyTorch/DrugDiscovery/SE3Transformer/LICENSE,https://github.com/NVIDIA/DeepLearningExamples#graph-neural-networks,State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and..,21,True,"['educational', 'drug-discovery']",NVIDIA/DeepLearningExamples,https://github.com/NVIDIA/DeepLearningExamples,2018-05-02 17:04:05,2024-08-12 14:01:29.000,2024-04-04 13:37:56,1437.0,,3061.0,300.0,538.0,307.0,570.0,13129.0,,,,115.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +65,DIG: Dive into Graphs,,rep-learn,GPL-3.0,https://github.com/divelab/DIG,A library for graph deep learning research.,21,True,,divelab/DIG,https://github.com/divelab/DIG,2020-10-30 03:51:15,2024-07-15 07:18:56.000,2024-02-04 20:37:53,1083.0,,281.0,29.0,41.0,34.0,176.0,1828.0,2023-04-07 20:33:15.000,1.1.0,10.0,50.0,dive-into-graphs,,,,https://pypi.org/project/dive-into-graphs,2022-06-27 05:08:24.000,,411.0,411.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +66,FAIR Chemistry datasets,,datasets,MIT,https://github.com/FAIR-Chem/fairchem,"Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project.",21,True,['catalysis'],FAIR-Chem/fairchem,https://github.com/FAIR-Chem/fairchem,2019-09-26 04:47:27,2024-08-17 07:23:58.000,2024-08-17 01:02:33,825.0,63.0,229.0,24.0,618.0,13.0,187.0,749.0,2024-08-14 16:52:55.000,fairchem_demo_ocpapi-0.1.0,8.0,41.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,, +67,FLARE,,active-learning,MIT,https://github.com/mir-group/flare,An open-source Python package for creating fast and accurate interatomic potentials.,21,True,"['lang-cpp', 'ml-iap']",mir-group/flare,https://github.com/mir-group/flare,2018-08-30 23:40:56,2024-08-15 19:57:53.000,2024-08-15 19:57:53,4412.0,15.0,65.0,20.0,187.0,36.0,170.0,283.0,2024-03-25 15:48:12.000,1.3.0,6.0,36.0,,,11.0,11.0,,,,,0.0,,,,1.0,,,,,,7.0,,,,,,,,,,,,,, +68,e3nn-jax,,rep-learn,Apache-2.0,https://github.com/e3nn/e3nn-jax,jax library for E3 Equivariant Neural Networks.,21,True,,e3nn/e3nn-jax,https://github.com/e3nn/e3nn-jax,2021-06-08 13:21:51,2024-08-14 05:15:15.000,2024-08-14 05:13:07,1000.0,15.0,18.0,12.0,48.0,1.0,19.0,173.0,2024-08-14 05:14:56.000,0.20.7,43.0,7.0,e3nn-jax,,49.0,36.0,https://pypi.org/project/e3nn-jax,2024-08-14 05:15:15.000,13.0,2623.0,2623.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +69,Metatensor,,data-structures,BSD-3-Clause,https://github.com/lab-cosmo/metatensor,Self-describing sparse tensor data format for atomistic machine learning and beyond.,21,True,"['lang-rust', 'lang-c', 'lang-cpp', 'lang-py']",lab-cosmo/metatensor,https://github.com/lab-cosmo/metatensor,2022-03-01 15:58:28,2024-08-15 14:39:40.000,2024-08-15 14:39:40,781.0,74.0,15.0,18.0,514.0,77.0,131.0,47.0,2024-07-15 12:55:26.000,metatensor-torch-v0.5.3,38.0,21.0,,,11.0,11.0,,,,,2442.0,,,,2.0,,,,,,24422.0,,,,,,,,,,,,,, +70,DM21,,ml-dft,Apache-2.0,https://github.com/google-deepmind/deepmind-research/tree/master/density_functional_approximation_dm21,This package provides a PySCF interface to the DM21 (DeepMind 21) family of exchange-correlation functionals described..,20,False,,google-deepmind/deepmind-research,https://github.com/google-deepmind/deepmind-research,2019-01-15 09:54:13,2024-08-09 16:59:25.000,2023-06-02 17:04:50,369.0,,2541.0,325.0,236.0,240.0,138.0,13015.0,,,,92.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,, +71,ALIGNN,,rep-learn,https://github.com/usnistgov/alignn/blob/main/LICENSE.rst,https://github.com/usnistgov/alignn,Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en.,20,True,,usnistgov/alignn,https://github.com/usnistgov/alignn,2021-04-19 20:08:09,2024-07-29 12:39:30.000,2024-07-15 20:09:42,707.0,15.0,78.0,11.0,107.0,34.0,24.0,205.0,2024-06-28 03:31:00.000,2024.5.27,46.0,7.0,alignn,,18.0,14.0,https://pypi.org/project/alignn,2024-06-28 03:31:00.000,4.0,974.0,974.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +72,fairchem,,ml-iap,,https://github.com/FAIR-Chem/fairchem,FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project (ocp).,19,True,"['pretrained', 'rep-learn', 'catalysis']",FAIR-Chem/fairchem,https://github.com/FAIR-Chem/fairchem,2019-09-26 04:47:27,2024-08-17 07:23:58.000,2024-08-17 01:02:33,825.0,63.0,229.0,24.0,618.0,13.0,187.0,749.0,2024-08-14 16:52:55.000,fairchem_demo_ocpapi-0.1.0,8.0,41.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +73,MACE-MP,,uip,MIT,https://github.com/ACEsuit/mace-mp,Pretrained foundation models for materials chemistry.,19,True,"['ml-iap', 'pretrained', 'rep-learn', 'md']",ACEsuit/mace-mp,https://github.com/ACEsuit/mace-mp,2024-01-11 10:55:55,2024-07-16 11:55:19.000,2024-04-24 14:56:12,10.0,,164.0,10.0,1.0,1.0,6.0,425.0,2024-07-16 11:55:19.000,0.3.6,4.0,2.0,mace-torch,,12.0,,https://pypi.org/project/mace-torch,2024-07-16 11:55:19.000,12.0,9716.0,12817.0,,,,2.0,,,,,,21709.0,,,,,,,,,,,,,, +74,exmol,,xai,MIT,https://github.com/ur-whitelab/exmol,Explainer for black box models that predict molecule properties.,19,True,,ur-whitelab/exmol,https://github.com/ur-whitelab/exmol,2021-08-03 17:56:06,2024-06-02 00:38:18.000,2023-12-04 18:03:57,189.0,,41.0,9.0,77.0,11.0,58.0,278.0,2023-06-19 20:58:01.262,3.0.3,27.0,7.0,exmol,,21.0,20.0,https://pypi.org/project/exmol,2022-06-03 18:52:10.000,1.0,688.0,688.0,,,,1.0,,,,,,,,,,,,,,,,,,,, +75,KFAC-JAX,,math,Apache-2.0,https://github.com/google-deepmind/kfac-jax,Second Order Optimization and Curvature Estimation with K-FAC in JAX.,19,True,,google-deepmind/kfac-jax,https://github.com/google-deepmind/kfac-jax,2022-03-18 10:19:24,2024-08-19 12:13:06.000,2024-08-19 12:13:00,223.0,22.0,16.0,12.0,243.0,9.0,9.0,225.0,2024-04-04 10:59:13.000,0.0.6,5.0,13.0,kfac-jax,,11.0,10.0,https://pypi.org/project/kfac-jax,2024-04-04 10:59:13.000,1.0,747.0,747.0,,,,1.0,,,,,,,,,,,,,,,,,,,, +76,Chemiscope,,visualization,BSD-3-Clause,https://github.com/lab-cosmo/chemiscope,An interactive structure/property explorer for materials and molecules.,19,True,['lang-js'],lab-cosmo/chemiscope,https://github.com/lab-cosmo/chemiscope,2019-10-03 09:59:42,2024-08-13 16:40:24.000,2024-08-05 21:35:00,730.0,22.0,29.0,19.0,238.0,36.0,86.0,122.0,2024-06-14 16:42:03.000,0.7.1,16.0,22.0,,,9.0,6.0,,,,,47.0,,,,3.0,,,,,,249.0,,,,,,chemiscope,https://www.npmjs.com/package/chemiscope,2023-03-15 15:39:26.701,3.0,43.0,,,, +77,MAST-ML,,general-tool,MIT,https://github.com/uw-cmg/MAST-ML,MAterials Simulation Toolkit for Machine Learning (MAST-ML).,19,True,,uw-cmg/MAST-ML,https://github.com/uw-cmg/MAST-ML,2017-02-16 17:03:57,2024-08-05 20:49:39.000,2024-04-17 20:51:19,3296.0,,58.0,14.0,37.0,32.0,191.0,101.0,2024-04-17 21:30:14.000,3.2.0,7.0,18.0,,,43.0,43.0,,,,,2.0,,,,2.0,,,,,,95.0,,,,,,,,,,,,,, +78,mlcolvar,,md,MIT,https://github.com/luigibonati/mlcolvar,A unified framework for machine learning collective variables for enhanced sampling simulations.,19,True,['enhanced-sampling'],luigibonati/mlcolvar,https://github.com/luigibonati/mlcolvar,2021-09-21 21:32:04,2024-07-31 15:12:33.000,2024-06-14 14:38:42,1089.0,55.0,24.0,7.0,80.0,13.0,57.0,91.0,2024-06-12 17:08:54.000,1.1.1,10.0,8.0,mlcolvar,,2.0,2.0,https://pypi.org/project/mlcolvar,2024-06-12 17:08:54.000,,152.0,152.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +79,MALA,,ml-dft,BSD-3-Clause,https://github.com/mala-project/mala,Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data.,19,True,,mala-project/mala,https://github.com/mala-project/mala,2021-03-31 11:40:38,2024-08-14 11:15:04.000,2024-07-04 09:53:01,2307.0,92.0,23.0,9.0,303.0,37.0,230.0,80.0,2024-02-01 08:57:56.000,1.2.1,9.0,44.0,,,1.0,1.0,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,, +80,Scikit-Matter,,general-tool,BSD-3-Clause,https://github.com/scikit-learn-contrib/scikit-matter,A collection of scikit-learn compatible utilities that implement methods born out of the materials science and..,19,True,['scikit-learn'],scikit-learn-contrib/scikit-matter,https://github.com/scikit-learn-contrib/scikit-matter,2020-10-12 19:23:26,2024-08-12 16:25:31.000,2024-08-06 07:51:07,386.0,18.0,20.0,17.0,162.0,14.0,56.0,73.0,2023-08-24 17:18:49.000,0.2.0,7.0,15.0,skmatter,conda-forge/skmatter,10.0,10.0,https://pypi.org/project/skmatter,2023-08-24 17:18:19.000,,990.0,1103.0,https://anaconda.org/conda-forge/skmatter,2023-08-24 19:08:29.551,1932.0,2.0,,,,,,,,,,,,,,,,,,,, +81,ZnDraw,,visualization,EPL-2.0,https://github.com/zincware/ZnDraw,"A powerful tool for visualizing, modifying, and analysing atomistic systems.",19,True,"['md', 'generative', 'lang-js']",zincware/ZnDraw,https://github.com/zincware/ZnDraw,2023-04-12 15:01:21,2024-08-19 08:48:54.000,2024-08-14 07:17:45,381.0,81.0,2.0,1.0,341.0,100.0,208.0,29.0,2024-08-14 07:17:12.000,0.4.6,55.0,7.0,zndraw,,5.0,3.0,https://pypi.org/project/zndraw,2024-08-14 07:18:10.000,2.0,1385.0,1385.0,,,,3.0,,,,,,,,,,,,,,,,,,,, +82,Graph-based Deep Learning Literature,,community,MIT,https://github.com/naganandy/graph-based-deep-learning-literature,links to conference publications in graph-based deep learning.,18,True,"['general-ml', 'rep-learn']",naganandy/graph-based-deep-learning-literature,https://github.com/naganandy/graph-based-deep-learning-literature,2017-12-01 14:48:35,2024-08-17 13:14:37.000,2024-08-17 13:14:32,7716.0,36.0,737.0,247.0,21.0,,14.0,4715.0,,,,12.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,, +83,Uni-Mol,,rep-learn,MIT,https://github.com/deepmodeling/Uni-Mol,Official Repository for the Uni-Mol Series Methods.,18,True,['pretrained'],deepmodeling/Uni-Mol,https://github.com/deepmodeling/Uni-Mol,2022-05-22 13:26:41,2024-08-14 10:40:57.000,2024-08-14 10:40:52,130.0,19.0,117.0,15.0,111.0,60.0,88.0,638.0,2024-07-06 07:05:10.000,0.2.1,3.0,16.0,,,,,,,,,661.0,,,,2.0,,,,,,14544.0,,,,,,,,,,,,,, +84,GT4SD,,generative,MIT,https://github.com/GT4SD/gt4sd-core,"GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process.",18,True,"['pretrained', 'drug-discovery', 'rep-learn']",GT4SD/gt4sd-core,https://github.com/GT4SD/gt4sd-core,2022-02-11 19:06:58,2024-07-04 11:11:27.000,2024-07-04 11:11:26,296.0,8.0,66.0,17.0,148.0,14.0,98.0,327.0,2024-06-13 15:18:45.000,1.4.1,85.0,20.0,gt4sd,,,,https://pypi.org/project/gt4sd,2024-06-13 13:19:51.000,,852.0,852.0,,,,1.0,,,,,,,,,,,,,,,,,,,, +85,ATOM3D,,datasets,MIT,https://github.com/drorlab/atom3d,ATOM3D: tasks on molecules in three dimensions.,18,False,"['biomolecules', 'benchmarking']",drorlab/atom3d,https://github.com/drorlab/atom3d,2020-04-03 22:53:11,2023-03-02 18:21:02.000,2023-03-02 18:20:29,798.0,,35.0,17.0,6.0,21.0,40.0,294.0,2022-07-20 00:58:03.115,0.2.6,15.0,10.0,atom3d,,40.0,40.0,https://pypi.org/project/atom3d,2022-07-20 00:58:03.115,,533.0,533.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +86,Neural Force Field,,ml-iap,MIT,https://github.com/learningmatter-mit/NeuralForceField,Neural Network Force Field based on PyTorch.,18,True,['pretrained'],learningmatter-mit/NeuralForceField,https://github.com/learningmatter-mit/NeuralForceField,2020-10-04 15:17:41,2024-08-11 22:33:54.000,2024-07-24 20:30:53,3121.0,74.0,47.0,7.0,5.0,1.0,18.0,225.0,2024-05-29 21:15:00.000,1.0.0,1.0,40.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +87,gpax,,math,MIT,https://github.com/ziatdinovmax/gpax,Gaussian Processes for Experimental Sciences.,18,True,"['probabilistic', 'active-learning']",ziatdinovmax/gpax,https://github.com/ziatdinovmax/gpax,2021-10-28 13:43:18,2024-08-09 21:37:33.000,2024-05-21 08:13:54,787.0,,22.0,7.0,68.0,8.0,32.0,195.0,2024-03-20 06:39:54.000,0.1.8,16.0,6.0,gpax,,1.0,1.0,https://pypi.org/project/gpax,2024-03-20 06:39:54.000,,168.0,168.0,,,,1.0,,,,,,,,,,,,,,,,,,,, +88,MatBench,,community,MIT,https://github.com/materialsproject/matbench,Matbench: Benchmarks for materials science property prediction.,18,True,"['datasets', 'benchmarking', 'model-repository']",materialsproject/matbench,https://github.com/materialsproject/matbench,2021-02-24 03:58:42,2024-06-26 23:31:08.000,2024-01-20 09:41:36,772.0,,42.0,8.0,297.0,39.0,26.0,105.0,2022-07-27 04:40:26.000,0.6,5.0,25.0,matbench,,18.0,16.0,https://pypi.org/project/matbench,2022-07-27 04:44:21.961,2.0,457.0,457.0,,,,1.0,,,,,,,,,,,,,,,,,,,, +89,kgcnn,,rep-learn,MIT,https://github.com/aimat-lab/gcnn_keras,"Graph convolutions in Keras with TensorFlow, PyTorch or Jax.",18,True,,aimat-lab/gcnn_keras,https://github.com/aimat-lab/gcnn_keras,2020-07-17 11:12:46,2024-05-06 10:08:41.000,2024-05-06 10:08:14,3099.0,,29.0,7.0,30.0,12.0,74.0,103.0,2024-02-27 12:33:28.000,4.0.1,28.0,7.0,kgcnn,,21.0,18.0,https://pypi.org/project/kgcnn,2024-02-27 12:33:28.000,3.0,270.0,270.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +90,apax,,ml-iap,MIT,https://github.com/apax-hub/apax,A flexible and performant framework for training machine learning potentials.,18,True,,apax-hub/apax,https://github.com/apax-hub/apax,2022-11-18 12:31:19,2024-08-12 22:21:46.000,2024-08-09 15:26:47,1705.0,245.0,1.0,4.0,209.0,14.0,101.0,14.0,2024-08-09 20:31:40.000,0.6.0,6.0,7.0,apax,,2.0,2.0,https://pypi.org/project/apax,2024-08-09 20:31:40.000,,307.0,307.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +91,DeepQMC,,ml-wft,MIT,https://github.com/deepqmc/deepqmc,Deep learning quantum Monte Carlo for electrons in real space.,17,True,,deepqmc/deepqmc,https://github.com/deepqmc/deepqmc,2019-12-06 14:50:59,2024-08-14 09:04:29.000,2024-08-14 09:04:27,1461.0,1.0,58.0,23.0,156.0,5.0,39.0,341.0,2023-11-20 10:38:40.000,1.1.2,11.0,13.0,deepqmc,,2.0,2.0,https://pypi.org/project/deepqmc,2023-11-20 10:38:40.000,,141.0,141.0,,,,1.0,,,,,,,,,,,,,,,,,,,, +92,escnn,,rep-learn,https://github.com/QUVA-Lab/escnn/blob/master/LICENSE,https://github.com/QUVA-Lab/escnn,Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/.,17,True,,QUVA-Lab/escnn,https://github.com/QUVA-Lab/escnn,2022-03-16 10:15:02,2024-06-26 14:25:20.000,2023-10-17 22:37:11,244.0,,42.0,18.0,33.0,38.0,36.0,339.0,2023-07-17 22:58:13.120,1.0.11,16.0,10.0,escnn,,6.0,,https://pypi.org/project/escnn,2022-04-01 11:46:00.000,6.0,504.0,504.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +93,M3GNet,,uip,BSD-3-Clause,https://github.com/materialsvirtuallab/m3gnet,Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art..,17,False,"['ml-iap', 'pretrained']",materialsvirtuallab/m3gnet,https://github.com/materialsvirtuallab/m3gnet,2022-01-18 18:10:58,2023-06-06 23:56:08.000,2023-06-06 23:56:03,261.0,,58.0,11.0,33.0,15.0,20.0,229.0,2022-11-17 23:25:35.000,0.2.4,16.0,15.0,m3gnet,,28.0,23.0,https://pypi.org/project/m3gnet,2022-11-17 23:25:34.805,5.0,626.0,626.0,,,,3.0,,,,,,,,,,,,,,,,,,,, +94,ChemNLP project,,language-models,MIT,https://github.com/OpenBioML/chemnlp,ChemNLP project.,17,True,['datasets'],OpenBioML/chemnlp,https://github.com/OpenBioML/chemnlp,2023-02-13 16:20:23,2024-08-15 21:04:45.000,2024-08-15 21:02:30,371.0,40.0,45.0,3.0,284.0,111.0,140.0,144.0,2023-08-07 12:49:57.000,2023.7.1,6.0,27.0,chemnlp,,1.0,,https://pypi.org/project/chemnlp,2023-08-07 12:49:57.000,1.0,72.0,72.0,,,,1.0,,,,,,,,,,,,,,,,,,,, +95,FitSNAP,,md,GPL-2.0,https://github.com/FitSNAP/FitSNAP,Software for generating SNAP machine-learning interatomic potentials.,17,True,,FitSNAP/FitSNAP,https://github.com/FitSNAP/FitSNAP,2019-09-12 14:46:18,2024-08-09 16:04:42.000,2024-08-09 16:04:41,1388.0,9.0,50.0,7.0,182.0,16.0,56.0,142.0,2023-06-28 16:00:48.000,3.1.0,7.0,24.0,,conda-forge/fitsnap3,2.0,2.0,,,,,169.0,https://anaconda.org/conda-forge/fitsnap3,2023-06-16 00:19:04.615,7640.0,2.0,,,,,,9.0,,,,,,,,,,,,,, +96,matsciml,,rep-learn,MIT,https://github.com/IntelLabs/matsciml,Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery..,17,True,"['workflows', 'benchmarking']",IntelLabs/matsciml,https://github.com/IntelLabs/matsciml,2022-09-13 20:27:28,2024-08-16 01:07:03.000,2024-08-15 22:36:29,2374.0,225.0,17.0,5.0,216.0,20.0,35.0,135.0,2023-08-31 23:59:40.000,1.0.0,2.0,11.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +97,DADApy,,unsupervised,Apache-2.0,https://github.com/sissa-data-science/DADApy,Distance-based Analysis of DAta-manifolds in python.,17,True,,sissa-data-science/DADApy,https://github.com/sissa-data-science/DADApy,2021-02-16 17:45:23,2024-08-08 11:25:55.000,2024-07-24 22:45:27,825.0,36.0,17.0,7.0,110.0,9.0,27.0,101.0,2024-07-02 15:52:45.000,0.3.1,6.0,19.0,dadapy,,7.0,7.0,https://pypi.org/project/dadapy,2024-07-02 15:49:35.000,,131.0,131.0,,,,1.0,,,,,,,,,,,,,,,,,,,, +98,CatLearn,,rep-eng,GPL-3.0,https://github.com/SUNCAT-Center/CatLearn,,17,False,['surface-science'],SUNCAT-Center/CatLearn,https://github.com/SUNCAT-Center/CatLearn,2018-04-20 04:16:14,2024-06-28 07:53:45.000,2023-02-07 09:31:25,1960.0,,57.0,19.0,80.0,10.0,17.0,100.0,2020-03-27 09:27:26.000,0.6.2,27.0,22.0,catlearn,,6.0,5.0,https://pypi.org/project/catlearn,2020-03-27 09:27:26.000,1.0,170.0,170.0,,,,1.0,,,,,,,,,,,,,,,,,,,, +99,Open Databases Integration for Materials Design (OPTIMADE),,datasets,CC-BY-4.0,https://github.com/Materials-Consortia/OPTIMADE,Specification of a common REST API for access to materials databases.,17,True,,Materials-Consortia/OPTIMADE,https://github.com/Materials-Consortia/OPTIMADE,2018-01-08 23:32:29,2024-07-29 21:09:07.000,2024-06-12 09:31:09,1786.0,2.0,35.0,21.0,297.0,68.0,169.0,76.0,2024-06-10 16:32:29.000,1.2.0,9.0,21.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +100,Graphormer,,rep-learn,MIT,https://github.com/microsoft/Graphormer,Graphormer is a general-purpose deep learning backbone for molecular modeling.,16,True,"['transformer', 'pretrained']",microsoft/Graphormer,https://github.com/microsoft/Graphormer,2021-05-27 05:31:18,2024-06-07 17:01:35.000,2024-05-28 06:22:34,77.0,2.0,331.0,31.0,46.0,92.0,64.0,2045.0,2024-04-03 08:23:10.000,dig-v1.0,2.0,14.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +101,OpenML,,community,BSD-3,https://github.com/openml/OpenML,Open Machine Learning.,16,True,['datasets'],openml/OpenML,https://github.com/openml/OpenML,2012-12-11 11:27:40,2024-04-05 08:51:24.000,2024-01-12 08:40:06,2317.0,,90.0,48.0,202.0,367.0,560.0,656.0,,,,35.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +102,ChemCrow,,language-models,MIT,https://github.com/ur-whitelab/chemcrow-public,Open source package for the accurate solution of reasoning-intensive chemical tasks.,16,True,['ai-agent'],ur-whitelab/chemcrow-public,https://github.com/ur-whitelab/chemcrow-public,2023-06-04 15:59:05,2024-04-03 19:49:19.000,2024-03-27 04:32:41,110.0,,80.0,16.0,22.0,6.0,14.0,559.0,2024-03-27 04:30:13.000,0.3.24,27.0,3.0,chemcrow,,4.0,4.0,https://pypi.org/project/chemcrow,2024-03-27 04:30:13.000,,546.0,546.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +103,GT4SD - Generative Toolkit for Scientific Discovery,,community,MIT,https://huggingface.co/GT4SD,Gradio apps of generative models in GT4SD.,16,True,"['generative', 'pretrained', 'drug-discovery', 'model-repository']",GT4SD/gt4sd-core,https://github.com/GT4SD/gt4sd-core,2022-02-11 19:06:58,2024-07-04 11:11:27.000,2024-07-04 11:11:26,296.0,8.0,66.0,17.0,148.0,14.0,98.0,327.0,2024-06-13 15:18:45.000,1.4.1,57.0,20.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +104,ChemDataExtractor,,language-models,MIT,https://github.com/mcs07/ChemDataExtractor,Automatically extract chemical information from scientific documents.,16,False,['literature-data'],mcs07/ChemDataExtractor,https://github.com/mcs07/ChemDataExtractor,2016-10-02 23:50:01,2023-07-27 18:05:13.000,2017-02-21 23:20:23,106.0,,106.0,18.0,16.0,21.0,9.0,296.0,2017-02-03 00:28:29.000,1.3.0,8.0,2.0,chemdataextractor,chemdataextractor/chemdataextractor,120.0,112.0,https://pypi.org/project/chemdataextractor,2017-02-03 00:12:36.000,8.0,696.0,760.0,https://anaconda.org/chemdataextractor/chemdataextractor,2023-06-16 13:17:47.249,3140.0,2.0,,,,,,2996.0,,,,,,,,,,,,,, +105,QML,,general-tool,MIT,https://github.com/qmlcode/qml,QML: Quantum Machine Learning.,16,False,,qmlcode/qml,https://github.com/qmlcode/qml,2017-04-22 04:48:38,2024-04-12 13:38:21.000,2018-09-10 11:14:35,75.0,,81.0,23.0,101.0,30.0,19.0,197.0,2018-03-02 11:36:41.000,0.4.0,34.0,2.0,qml,,31.0,31.0,https://pypi.org/project/qml,2018-08-13 10:37:42.000,,423.0,423.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +106,openmm-torch,,md,https://github.com/openmm/openmm-torch#license,https://github.com/openmm/openmm-torch,OpenMM plugin to define forces with neural networks.,16,True,"['ml-iap', 'lang-cpp']",openmm/openmm-torch,https://github.com/openmm/openmm-torch,2019-09-27 18:15:19,2024-08-16 16:00:02.000,2024-08-16 16:00:01,72.0,5.0,24.0,12.0,61.0,25.0,65.0,177.0,2023-10-09 08:49:10.000,1.4,16.0,8.0,,conda-forge/openmm-torch,,,,,,,9395.0,https://anaconda.org/conda-forge/openmm-torch,2024-06-03 12:02:18.688,404005.0,2.0,,,,,,,,,,,,,,,,,,,, +107,sGDML,,ml-iap,MIT,https://github.com/stefanch/sGDML,sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model.,16,True,,stefanch/sGDML,https://github.com/stefanch/sGDML,2018-07-11 15:20:30,2023-08-31 12:59:32.000,2023-08-31 12:57:53,205.0,,35.0,8.0,12.0,6.0,11.0,139.0,2023-08-31 12:58:49.000,1.0.2,21.0,8.0,sgdml,,10.0,9.0,https://pypi.org/project/sgdml,2023-08-31 12:59:32.000,1.0,200.0,200.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +108,XenonPy,,general-tool,BSD-3-Clause,https://github.com/yoshida-lab/XenonPy,XenonPy is a Python Software for Materials Informatics.,16,True,,yoshida-lab/XenonPy,https://github.com/yoshida-lab/XenonPy,2018-01-17 10:13:29,2024-07-15 21:14:48.000,2024-04-21 06:58:38,693.0,,57.0,11.0,184.0,21.0,66.0,131.0,2023-05-21 15:54:32.000,0.6.8,54.0,10.0,xenonpy,,1.0,,https://pypi.org/project/xenonpy,2022-10-31 15:40:18.355,1.0,265.0,283.0,,,,2.0,,,,,,1393.0,,,,,,,,,,,,,, +109,MatBench Discovery,,community,MIT,https://github.com/janosh/matbench-discovery,An evaluation framework for machine learning models simulating high-throughput materials discovery.,16,True,"['datasets', 'benchmarking', 'model-repository']",janosh/matbench-discovery,https://github.com/janosh/matbench-discovery,2022-06-20 18:32:44,2024-08-19 12:23:31.000,2024-08-19 12:23:18,359.0,25.0,11.0,8.0,77.0,2.0,33.0,82.0,2024-07-15 20:08:07.000,1.2.0,8.0,7.0,matbench-discovery,,2.0,2.0,https://pypi.org/project/matbench-discovery,2024-07-15 20:08:07.000,,148.0,148.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +110,PMTransformer,,generative,MIT,https://github.com/hspark1212/MOFTransformer,"Universal Transfer Learning in Porous Materials, including MOFs.",16,True,"['transfer-learning', 'pretrained', 'transformer']",hspark1212/MOFTransformer,https://github.com/hspark1212/MOFTransformer,2021-12-11 06:30:12,2024-06-20 07:01:44.000,2024-06-20 06:57:57,410.0,6.0,11.0,5.0,127.0,,36.0,82.0,2024-06-20 07:02:24.000,2.2.0,17.0,2.0,moftransformer,,7.0,6.0,https://pypi.org/project/moftransformer,2024-06-20 07:01:44.000,1.0,320.0,320.0,,,,1.0,,,,,,,,,,,,,,,,,,,, +111,SpheriCart,,math,MIT,https://github.com/lab-cosmo/sphericart,Multi-language library for the calculation of spherical harmonics in Cartesian coordinates.,16,True,,lab-cosmo/sphericart,https://github.com/lab-cosmo/sphericart,2023-02-04 15:15:25,2024-08-19 12:41:01.000,2024-08-19 11:39:37,366.0,18.0,10.0,5.0,106.0,19.0,13.0,63.0,2023-04-27 09:57:29.640,0.3.0,10.0,10.0,sphericart,,3.0,3.0,https://pypi.org/project/sphericart,2024-01-23 17:06:36.000,,198.0,201.0,,,,2.0,,,,,,60.0,,,,,,,,,,,,,, +112,mp-pyrho,,data-structures,https://github.com/materialsproject/pyrho,https://github.com/materialsproject/pyrho,Tools for re-griding volumetric quantum chemistry data for machine-learning purposes.,16,True,['ml-dft'],materialsproject/pyrho,https://github.com/materialsproject/pyrho,2020-05-25 22:44:02,2024-08-14 00:15:19.000,2024-02-23 02:53:46,287.0,,6.0,9.0,115.0,1.0,3.0,36.0,2024-02-23 02:55:26.000,0.4.4,28.0,8.0,mp-pyrho,,26.0,23.0,https://pypi.org/project/mp-pyrho,2024-02-23 02:55:26.000,3.0,5825.0,5825.0,,,,3.0,,,,,,,,,,,,,,,,,,,, +113,wfl,,ml-iap,GPL-2.0,https://github.com/libAtoms/workflow,Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows.,16,True,"['workflows', 'htc']",libAtoms/workflow,https://github.com/libAtoms/workflow,2021-11-04 17:03:34,2024-08-16 13:18:11.000,2024-08-16 12:23:47,1153.0,109.0,18.0,9.0,180.0,67.0,91.0,27.0,2024-04-25 15:07:11.000,0.2.4,4.0,18.0,,,1.0,1.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +114,IPSuite,,active-learning,EPL-2.0,https://github.com/zincware/IPSuite,A Python toolkit for FAIR development and deployment of machine-learned interatomic potentials.,16,True,"['ml-iap', 'md', 'workflows', 'htc', 'FAIR']",zincware/IPSuite,https://github.com/zincware/IPSuite,2023-03-01 16:34:45,2024-08-16 11:34:27.000,2024-08-16 11:34:25,443.0,37.0,8.0,3.0,203.0,67.0,65.0,18.0,2024-08-08 20:37:20.000,0.1.3,7.0,8.0,ipsuite,,6.0,6.0,https://pypi.org/project/ipsuite,2024-08-08 20:37:48.000,,142.0,142.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +115,MoLeR,,generative,MIT,https://github.com/microsoft/molecule-generation,Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation.,15,True,,microsoft/molecule-generation,https://github.com/microsoft/molecule-generation,2022-02-17 19:16:29,2024-01-05 14:31:05.000,2024-01-03 14:28:02,67.0,,38.0,11.0,37.0,10.0,28.0,250.0,2024-01-05 14:31:05.000,0.4.1,5.0,5.0,molecule-generation,,1.0,,https://pypi.org/project/molecule-generation,2024-01-05 14:31:05.000,1.0,174.0,174.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +116,Automatminer,,general-tool,https://github.com/hackingmaterials/automatminer/blob/main/LICENSE,https://github.com/hackingmaterials/automatminer,An automatic engine for predicting materials properties.,15,False,['automl'],hackingmaterials/automatminer,https://github.com/hackingmaterials/automatminer,2018-05-10 18:27:08,2023-11-12 10:09:39.000,2022-01-06 19:39:49,1666.0,,49.0,12.0,233.0,41.0,138.0,134.0,2020-07-28 02:19:07.000,1.0.3.20200727,17.0,13.0,automatminer,,8.0,8.0,https://pypi.org/project/automatminer,2020-07-28 02:23:45.000,,205.0,205.0,,,,3.0,,,,,,,,,,,,,,,,,,,, +117,MODNet,,rep-eng,MIT,https://github.com/ppdebreuck/modnet,MODNet: a framework for machine learning materials properties.,15,True,"['pretrained', 'small-data', 'transfer-learning']",ppdebreuck/modnet,https://github.com/ppdebreuck/modnet,2020-03-13 07:39:21,2024-06-24 13:31:56.000,2024-06-24 12:29:45,276.0,1.0,32.0,7.0,174.0,26.0,27.0,74.0,2024-05-07 14:09:13.000,0.4.4,21.0,8.0,,,7.0,7.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +118,Ultra-Fast Force Fields (UF3),,ml-iap,Apache-2.0,https://github.com/uf3/uf3,UF3: a python library for generating ultra-fast interatomic potentials.,15,True,,uf3/uf3,https://github.com/uf3/uf3,2021-10-01 13:21:44,2024-07-27 23:40:38.000,2024-07-12 22:56:23,703.0,21.0,20.0,6.0,82.0,19.0,30.0,57.0,2023-10-27 16:37:16.000,0.4.0,4.0,10.0,uf3,,1.0,1.0,https://pypi.org/project/uf3,2023-10-27 16:37:16.000,,44.0,44.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +119,aviary,,materials-discovery,MIT,https://github.com/CompRhys/aviary,The Wren sits on its Roost in the Aviary.,15,True,,CompRhys/aviary,https://github.com/CompRhys/aviary,2021-09-28 12:29:05,2024-08-05 20:10:13.000,2024-08-05 20:09:13,638.0,28.0,11.0,3.0,61.0,4.0,24.0,46.0,2024-07-22 19:03:03.000,1.0.0,5.0,4.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,, +120,KLIFF,,ml-iap,LGPL-2.1,https://github.com/openkim/kliff,KIM-based Learning-Integrated Fitting Framework for interatomic potentials.,15,True,"['probabilistic', 'workflows']",openkim/kliff,https://github.com/openkim/kliff,2017-08-01 20:33:58,2024-08-14 00:02:36.000,2024-07-06 01:33:46,1073.0,8.0,20.0,4.0,148.0,22.0,19.0,34.0,2023-12-17 02:12:19.000,0.4.3,18.0,9.0,kliff,conda-forge/kliff,3.0,3.0,https://pypi.org/project/kliff,2023-12-17 02:12:19.000,,96.0,2180.0,https://anaconda.org/conda-forge/kliff,2023-12-18 18:25:23.770,95873.0,2.0,,,,,,,,,,,,,,,,,,,, +121,Polynomials4ML.jl,,math,MIT,https://github.com/ACEsuit/Polynomials4ML.jl,"Polynomials for ML: fast evaluation, batching, differentiation.",15,True,['lang-julia'],ACEsuit/Polynomials4ML.jl,https://github.com/ACEsuit/Polynomials4ML.jl,2022-09-20 23:05:53,2024-08-18 21:49:29.000,2024-06-22 16:18:31,410.0,50.0,5.0,4.0,40.0,17.0,34.0,12.0,2024-06-22 16:34:35.000,0.3.1,17.0,10.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +122,benchmarking-gnns,,rep-learn,MIT,https://github.com/graphdeeplearning/benchmarking-gnns,Repository for benchmarking graph neural networks.,14,False,"['single-paper', 'benchmarking']",graphdeeplearning/benchmarking-gnns,https://github.com/graphdeeplearning/benchmarking-gnns,2020-03-03 03:42:50,2023-06-22 04:03:53.000,2022-05-10 13:22:20,45.0,,446.0,58.0,17.0,5.0,63.0,2477.0,,,,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +123,dlpack,,data-structures,Apache-2.0,https://github.com/dmlc/dlpack,common in-memory tensor structure.,14,True,['lang-cpp'],dmlc/dlpack,https://github.com/dmlc/dlpack,2017-02-24 16:56:47,2024-05-13 08:28:51.000,2024-03-26 12:57:17,75.0,,130.0,47.0,77.0,27.0,42.0,883.0,2023-01-05 18:42:00.000,0.8,9.0,23.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +124,FermiNet,,ml-wft,Apache-2.0,https://github.com/google-deepmind/ferminet,An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations.,14,True,['transformer'],google-deepmind/ferminet,https://github.com/google-deepmind/ferminet,2020-10-06 12:21:06,2024-06-04 15:46:46.000,2024-06-04 15:46:09,228.0,4.0,110.0,37.0,30.0,1.0,44.0,659.0,,,,18.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +125,SevenNet,,uip,GPL-3.0,https://github.com/MDIL-SNU/SevenNet,SevenNet (Scalable EquiVariance Enabled Neural Network) is a graph neural network interatomic potential package that..,14,True,"['ml-iap', 'md', 'pretrained']",MDIL-SNU/SevenNet,https://github.com/MDIL-SNU/SevenNet,2023-02-16 06:31:53,2024-08-19 07:35:26.000,2024-08-19 02:59:51,413.0,97.0,10.0,4.0,60.0,5.0,7.0,87.0,2024-07-26 01:40:27.000,0.9.3,6.0,8.0,,,1.0,1.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +126,PyXtalFF,,ml-iap,MIT,https://github.com/MaterSim/PyXtal_FF,Machine Learning Interatomic Potential Predictions.,14,True,,MaterSim/PyXtal_FF,https://github.com/MaterSim/PyXtal_FF,2019-01-08 08:43:35,2024-02-15 16:12:06.000,2024-01-07 14:27:45,561.0,,23.0,9.0,4.0,12.0,51.0,85.0,2023-06-09 17:17:24.000,0.2.3,19.0,9.0,pyxtal_ff,,,,https://pypi.org/project/pyxtal_ff,2022-12-21 20:21:00.409,,69.0,69.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +127,HydraGNN,,rep-learn,BSD-3,https://github.com/ORNL/HydraGNN,Distributed PyTorch implementation of multi-headed graph convolutional neural networks.,14,True,,ORNL/HydraGNN,https://github.com/ORNL/HydraGNN,2021-05-28 03:32:03,2024-08-19 01:37:13.000,2024-08-06 16:10:42,675.0,23.0,23.0,10.0,228.0,17.0,32.0,57.0,2023-11-10 15:25:43.000,3.0,2.0,13.0,,,1.0,1.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +128,SchNetPack G-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/schnetpack-gschnet,G-SchNet extension for SchNetPack.,14,True,,atomistic-machine-learning/schnetpack-gschnet,https://github.com/atomistic-machine-learning/schnetpack-gschnet,2022-04-21 12:34:13,2024-07-05 12:47:35.000,2024-07-05 12:44:53,164.0,31.0,8.0,4.0,1.0,,14.0,43.0,2024-07-03 16:43:48.000,1.1.0,3.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +129,load-atoms,,datasets,MIT,https://github.com/jla-gardner/load-atoms,download and manipulate atomistic datasets.,14,True,['data-structures'],jla-gardner/load-atoms,https://github.com/jla-gardner/load-atoms,2022-11-21 21:59:15,2024-04-06 05:14:57.000,2024-04-06 05:13:05,271.0,,2.0,1.0,31.0,2.0,29.0,37.0,2024-04-06 05:04:52.000,0.2.14,36.0,3.0,load-atoms,,3.0,3.0,https://pypi.org/project/load-atoms,2024-04-06 05:04:52.000,,256.0,256.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +130,SALTED,,ml-dft,GPL-3.0,https://github.com/andreagrisafi/SALTED,Symmetry-Adapted Learning of Three-dimensional Electron Densities.,14,True,,andreagrisafi/SALTED,https://github.com/andreagrisafi/SALTED,2020-01-22 10:24:29,2024-08-02 15:26:59.000,2024-07-08 10:41:32,699.0,49.0,4.0,3.0,39.0,1.0,5.0,30.0,2024-06-22 12:42:33.000,3.0.0,2.0,16.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,, +131,SISSO,,rep-eng,Apache-2.0,https://github.com/rouyang2017/SISSO,A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models.,13,True,['lang-fortran'],rouyang2017/SISSO,https://github.com/rouyang2017/SISSO,2017-10-16 11:31:57,2024-06-19 15:58:39.000,2023-09-12 08:50:38,166.0,,76.0,6.0,3.0,18.0,57.0,229.0,,,,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +132,n2p2,,ml-iap,GPL-3.0,https://github.com/CompPhysVienna/n2p2,n2p2 - A Neural Network Potential Package.,13,False,['lang-cpp'],CompPhysVienna/n2p2,https://github.com/CompPhysVienna/n2p2,2018-07-25 12:29:17,2023-05-11 16:26:02.000,2022-09-05 10:56:20,387.0,,75.0,12.0,53.0,68.0,85.0,216.0,2022-05-23 12:53:39.000,2.2.0,11.0,9.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +133,Librascal,,rep-eng,LGPL-2.1,https://github.com/lab-cosmo/librascal,A scalable and versatile library to generate representations for atomic-scale learning.,13,True,,lab-cosmo/librascal,https://github.com/lab-cosmo/librascal,2018-02-01 08:38:51,2024-01-11 17:38:31.000,2023-11-30 14:48:28,2931.0,,20.0,22.0,201.0,115.0,132.0,80.0,,,3.0,30.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +134,NNPOps,,ml-iap,MIT,https://github.com/openmm/NNPOps,High-performance operations for neural network potentials.,13,True,"['md', 'lang-cpp']",openmm/NNPOps,https://github.com/openmm/NNPOps,2020-09-10 21:02:00,2024-07-10 15:29:02.000,2024-07-10 15:29:02,95.0,1.0,17.0,9.0,63.0,21.0,34.0,80.0,2023-07-26 11:21:58.000,0.6,7.0,9.0,,conda-forge/nnpops,,,,,,,6495.0,https://anaconda.org/conda-forge/nnpops,2024-05-31 15:49:32.883,188378.0,2.0,,,,,,,,,,,,,,,,,,,, +135,OpenMM-ML,,md,MIT,https://github.com/openmm/openmm-ml,High level API for using machine learning models in OpenMM simulations.,13,True,['ml-iap'],openmm/openmm-ml,https://github.com/openmm/openmm-ml,2021-02-10 20:55:25,2024-08-06 19:01:36.000,2024-08-06 19:01:36,46.0,2.0,18.0,14.0,33.0,20.0,34.0,79.0,2024-06-06 16:49:09.000,1.2,6.0,5.0,,conda-forge/openmm-ml,,,,,,,193.0,https://anaconda.org/conda-forge/openmm-ml,2024-06-07 16:52:07.157,4641.0,3.0,,,,,,,,,,,,,,,,,,,, +136,MLatom,,general-tool,MIT,https://github.com/dralgroup/mlatom,AI-enhanced computational chemistry.,13,True,"['uip', 'ml-iap', 'md', 'ml-dft', 'ml-esm', 'transfer-learning', 'active-learning', 'spectroscopy', 'structure-optimization']",dralgroup/mlatom,https://github.com/dralgroup/mlatom,2023-08-16 13:47:48,2024-07-25 06:05:25.000,2024-07-24 04:47:50,52.0,12.0,6.0,2.0,18.0,,1.0,36.0,2024-07-23 08:38:48.000,3.9.0,15.0,3.0,mlatom,,,,https://pypi.org/project/mlatom,2024-07-25 06:05:25.000,,872.0,872.0,,,,3.0,,,,,,,,,,,,,,,,,,,, +137,flare++,,active-learning,MIT,https://github.com/mir-group/flare_pp,A many-body extension of the FLARE code.,13,False,"['lang-cpp', 'ml-iap']",mir-group/flare_pp,https://github.com/mir-group/flare_pp,2019-11-20 22:46:32,2022-02-27 21:05:09.000,2022-02-24 19:00:50,989.0,,6.0,7.0,28.0,8.0,17.0,35.0,2021-12-23 05:02:12.000,0.1.1,25.0,10.0,flare_pp,,2.0,,https://pypi.org/project/flare_pp,2021-12-23 05:02:12.000,2.0,205.0,205.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +138,GlassPy,,rep-eng,GPL-3.0,https://github.com/drcassar/glasspy,Python module for scientists working with glass materials.,13,True,,drcassar/glasspy,https://github.com/drcassar/glasspy,2019-07-18 23:15:43,2024-08-11 17:13:25.000,2024-01-21 13:59:55,334.0,,7.0,6.0,13.0,1.0,5.0,26.0,2024-01-21 14:30:47.000,0.4.6,11.0,1.0,glasspy,,5.0,5.0,https://pypi.org/project/glasspy,2024-01-21 14:30:47.000,,150.0,150.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +139,Crystal Graph Convolutional Neural Networks (CGCNN),,rep-learn,MIT,https://github.com/txie-93/cgcnn,Crystal graph convolutional neural networks for predicting material properties.,12,False,,txie-93/cgcnn,https://github.com/txie-93/cgcnn,2018-03-14 20:41:21,2021-09-06 05:23:51.000,2021-09-06 05:23:38,25.0,,273.0,23.0,7.0,17.0,20.0,623.0,,,,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +140,mat2vec,,language-models,MIT,https://github.com/materialsintelligence/mat2vec,Supplementary Materials for Tshitoyan et al. Unsupervised word embeddings capture latent knowledge from materials..,12,False,['rep-learn'],materialsintelligence/mat2vec,https://github.com/materialsintelligence/mat2vec,2019-04-25 07:55:30,2023-05-06 22:45:49.000,2023-05-06 22:45:49,55.0,,174.0,40.0,7.0,6.0,18.0,616.0,,,,5.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +141,AI for Science Resources,,community,GPL-3.0 license,https://github.com/divelab/AIRS/blob/main/Overview/resources.md,"List of resources for AI4Science research, including learning resources.",12,True,,divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2024-07-12 20:06:37.000,2024-07-12 20:06:37,425.0,4.0,57.0,18.0,5.0,2.0,12.0,478.0,,,,28.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +142,QH9,,datasets,CC-BY-NC-SA-4.0,https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench/QH9,A Quantum Hamiltonian Prediction Benchmark.,12,True,['ml-dft'],divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2024-07-12 20:06:37.000,2024-07-12 20:06:37,425.0,4.0,57.0,18.0,5.0,2.0,12.0,478.0,,,,28.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +143,Artificial Intelligence for Science (AIRS),,general-tool,GPL-3.0 license,https://github.com/divelab/AIRS,Artificial Intelligence Research for Science (AIRS).,12,True,"['rep-learn', 'generative', 'ml-iap', 'md', 'ml-dft', 'ml-wft', 'biomolecules']",divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2024-07-12 20:06:37.000,2024-07-12 20:06:37,425.0,4.0,57.0,18.0,5.0,2.0,12.0,478.0,,,,28.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +144,QHNet,,ml-dft,GPL-3.0,https://github.com/divelab/AIRS/tree/main/OpenDFT/QHNet,Artificial Intelligence Research for Science (AIRS).,12,True,['rep-learn'],divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2024-07-12 20:06:37.000,2024-07-12 20:06:37,425.0,4.0,57.0,18.0,5.0,2.0,12.0,478.0,,,,28.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +145,Geometric GNN Dojo,,educational,MIT,https://github.com/chaitjo/geometric-gnn-dojo/blob/main/geometric_gnn_101.ipynb,"New to geometric GNNs: try our practical notebook, prepared for MPhil students at the University of Cambridge.",12,False,['rep-learn'],chaitjo/geometric-gnn-dojo,https://github.com/chaitjo/geometric-gnn-dojo,2023-01-21 20:08:45,2024-05-22 11:06:03.000,2023-06-18 23:17:32,26.0,,44.0,10.0,5.0,3.0,6.0,447.0,2023-06-18 23:20:44.000,0.2.0,2.0,3.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,, +146,DeepLearningLifeSciences,,educational,MIT,https://github.com/deepchem/DeepLearningLifeSciences,Example code from the book Deep Learning for the Life Sciences.,12,False,,deepchem/DeepLearningLifeSciences,https://github.com/deepchem/DeepLearningLifeSciences,2019-02-05 17:16:18,2021-09-17 05:10:37.000,2021-09-17 05:10:37,52.0,,151.0,25.0,15.0,11.0,10.0,345.0,2019-10-28 18:46:28.000,1.0,1.0,10.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,, +147,TensorMol,,ml-iap,GPL-3.0,https://github.com/jparkhill/TensorMol,Tensorflow + Molecules = TensorMol.,12,False,['single-paper'],jparkhill/TensorMol,https://github.com/jparkhill/TensorMol,2016-10-28 19:40:11,2021-02-11 00:12:00.000,2018-03-30 12:26:14,1724.0,,74.0,45.0,8.0,18.0,19.0,270.0,2017-11-08 18:05:50.000,0.1,1.0,12.0,,,1.0,1.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +148,gptchem,,language-models,MIT,https://github.com/kjappelbaum/gptchem,Use GPT-3 to solve chemistry problems.,12,True,,kjappelbaum/gptchem,https://github.com/kjappelbaum/gptchem,2023-01-06 15:34:32,2024-05-17 19:25:11.000,2023-10-04 11:27:09,147.0,,39.0,9.0,5.0,19.0,2.0,221.0,2023-11-30 09:31:51.000,0.0.4,4.0,4.0,gptchem,,,,https://pypi.org/project/gptchem,2023-10-04 11:28:07.000,,36.0,36.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +149,ANI-1,,ml-iap,MIT,https://github.com/isayev/ASE_ANI,ANI-1 neural net potential with python interface (ASE).,12,True,,isayev/ASE_ANI,https://github.com/isayev/ASE_ANI,2016-12-08 05:09:32,2024-03-11 21:50:26.000,2024-03-11 21:50:26,112.0,,55.0,33.0,9.0,16.0,21.0,220.0,,,,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +150,DMFF,,ml-iap,LGPL-3.0,https://github.com/deepmodeling/DMFF,DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable..,12,True,,deepmodeling/DMFF,https://github.com/deepmodeling/DMFF,2022-02-14 01:35:50,2024-08-16 15:18:26.000,2024-01-12 00:58:20,431.0,,40.0,9.0,159.0,10.0,16.0,146.0,2023-11-09 14:32:37.000,1.0.0,4.0,14.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +151,ASAP,,unsupervised,MIT,https://github.com/BingqingCheng/ASAP,ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.,12,True,,BingqingCheng/ASAP,https://github.com/BingqingCheng/ASAP,2019-08-11 12:45:14,2024-06-27 12:53:17.000,2024-06-27 12:53:00,763.0,3.0,28.0,7.0,37.0,6.0,19.0,142.0,2023-08-30 13:54:23.000,1,1.0,6.0,,,6.0,6.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +152,SPICE,,datasets,MIT,https://github.com/openmm/spice-dataset,A collection of QM data for training potential functions.,12,True,"['ml-iap', 'md']",openmm/spice-dataset,https://github.com/openmm/spice-dataset,2021-08-31 18:52:05,2024-08-16 23:44:23.000,2024-04-15 20:14:31,42.0,,8.0,17.0,47.0,15.0,45.0,141.0,2024-04-15 20:17:14.000,2.0.1,8.0,1.0,,,,,,,,,9.0,,,,2.0,,,,,,244.0,,,,,,,,,,,,,, +153,So3krates (MLFF),,ml-iap,MIT,https://github.com/thorben-frank/mlff,Build neural networks for machine learning force fields with JAX.,12,True,,thorben-frank/mlff,https://github.com/thorben-frank/mlff,2022-09-30 07:40:17,2024-08-12 14:37:34.000,2024-08-06 09:40:56,138.0,7.0,13.0,7.0,21.0,3.0,6.0,69.0,2024-06-24 11:09:20.000,0.3.0,2.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +154,jarvis-tools-notebooks,,educational,NIST,https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks,A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/.,12,True,,JARVIS-Materials-Design/jarvis-tools-notebooks,https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks,2020-06-27 20:22:02,2024-08-14 02:50:36.000,2024-08-14 02:50:35,753.0,113.0,26.0,4.0,46.0,,,59.0,,,,5.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,, +155,Neural fingerprint (nfp),,rep-learn,https://github.com/NREL/nfp/blob/master/LICENSE,https://github.com/NREL/nfp,Keras layers for end-to-end learning with rdkit and pymatgen.,12,False,,NREL/nfp,https://github.com/NREL/nfp,2018-11-20 23:55:23,2024-02-24 20:11:49.000,2022-06-14 22:18:28,143.0,,27.0,7.0,19.0,2.0,6.0,57.0,2022-04-27 17:05:25.000,0.3.12,13.0,4.0,,,13.0,13.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +156,ChatMOF,,language-models,MIT,https://github.com/Yeonghun1675/ChatMOF,Predict and Inverse design for metal-organic framework with large-language models (llms).,12,True,['generative'],Yeonghun1675/ChatMOF,https://github.com/Yeonghun1675/ChatMOF,2023-05-19 06:33:06,2024-07-01 05:01:35.000,2024-07-01 04:57:36,72.0,4.0,7.0,1.0,12.0,,5.0,53.0,2024-06-14 09:56:27.000,0.2.1,17.0,1.0,chatmof,,2.0,2.0,https://pypi.org/project/chatmof,2024-07-01 05:01:35.000,,285.0,285.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +157,Rascaline,,rep-eng,BSD-3-Clause,https://github.com/Luthaf/rascaline,Computing representations for atomistic machine learning.,12,True,"['lang-rust', 'lang-cpp']",Luthaf/rascaline,https://github.com/Luthaf/rascaline,2020-09-24 14:28:34,2024-08-14 13:15:55.000,2024-08-14 13:10:20,561.0,17.0,13.0,7.0,255.0,32.0,35.0,44.0,,,,14.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +158,synspace,,generative,MIT,https://github.com/whitead/synspace,Synthesis generative model.,12,False,,whitead/synspace,https://github.com/whitead/synspace,2022-12-28 00:59:14,2023-04-15 22:42:57.000,2023-04-15 18:04:16,27.0,,3.0,3.0,1.0,3.0,1.0,35.0,2023-04-15 22:48:00.713,0.3.0,3.0,2.0,synspace,,18.0,17.0,https://pypi.org/project/synspace,2023-01-16 17:29:00.461,1.0,903.0,903.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +159,BOSS,,materials-discovery,Apache-2.0,https://gitlab.com/cest-group/boss,Bayesian Optimization Structure Search (BOSS).,12,True,['probabilistic'],,,2020-02-12 08:48:33,2024-07-20 17:27:04.000,,,,10.0,,,2.0,28.0,20.0,2024-05-03 13:49:43.000,1.10.1,49.0,,aalto-boss,,,,https://pypi.org/project/aalto-boss,2024-07-20 17:27:04.000,,591.0,591.0,,,,1.0,,,,,,,,,,,,,,,,,cest-group/boss,https://gitlab.com/cest-group/boss,, +160,Compositionally-Restricted Attention-Based Network (CrabNet),,rep-learn,MIT,https://github.com/sparks-baird/CrabNet,Predict materials properties using only the composition information!.,12,False,,sparks-baird/CrabNet,https://github.com/sparks-baird/CrabNet,2021-09-17 07:58:15,2023-06-19 09:35:52.000,2023-06-19 09:35:52,427.0,,4.0,1.0,54.0,15.0,2.0,12.0,2023-06-07 01:07:33.000,2.0.8,37.0,5.0,crabnet,,15.0,13.0,https://pypi.org/project/crabnet,2023-01-10 04:27:02.444,2.0,319.0,319.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +161,Deep Learning for Molecules and Materials Book,,educational,https://github.com/whitead/dmol-book/blob/main/LICENSE,https://dmol.pub/,Deep learning for molecules and materials book.,11,False,,whitead/dmol-book,https://github.com/whitead/dmol-book,2020-08-19 19:24:32,2023-07-02 18:02:57.000,2023-07-02 18:02:56,558.0,,111.0,16.0,92.0,28.0,129.0,604.0,,,,19.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +162,ReLeaSE,,reinforcement-learning,MIT,https://github.com/isayev/ReLeaSE,Deep Reinforcement Learning for de-novo Drug Design.,11,False,['drug-discovery'],isayev/ReLeaSE,https://github.com/isayev/ReLeaSE,2018-04-26 14:50:34,2021-12-08 19:49:36.000,2021-12-08 19:49:36,160.0,,130.0,19.0,9.0,27.0,8.0,347.0,,,,5.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,, +163,DeepH-pack,,ml-dft,LGPL-3.0,https://github.com/mzjb/DeepH-pack,Deep neural networks for density functional theory Hamiltonian.,11,True,['lang-julia'],mzjb/DeepH-pack,https://github.com/mzjb/DeepH-pack,2022-05-13 02:51:32,2024-05-22 10:50:01.000,2024-05-22 10:50:01,66.0,2.0,36.0,7.0,17.0,12.0,38.0,201.0,2023-07-11 08:13:06.000,0.2.2,2.0,8.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +164,PiNN,,ml-iap,BSD-3-Clause,https://github.com/Teoroo-CMC/PiNN,A Python library for building atomic neural networks.,11,True,,Teoroo-CMC/PiNN,https://github.com/Teoroo-CMC/PiNN,2019-10-04 08:13:18,2024-08-02 06:55:47.000,2024-06-27 11:23:15,160.0,2.0,31.0,6.0,15.0,1.0,5.0,104.0,2019-10-09 09:21:30.000,0.3.0,1.0,5.0,,,,,,,,,4.0,,,,2.0,teoroo/pinn,https://hub.docker.com/r/teoroo/pinn,2024-06-27 11:32:07.538231,,243.0,,,,,,,,,,,,,,, +165,tinker-hp,,ml-iap,https://github.com/TinkerTools/tinker-hp/blob/master/license-Tinker.pdf,https://github.com/TinkerTools/tinker-hp,Tinker-HP: High-Performance Massively Parallel Evolution of Tinker on CPUs & GPUs.,11,True,,TinkerTools/tinker-hp,https://github.com/TinkerTools/tinker-hp,2018-06-12 12:15:51,2024-08-05 15:47:54.000,2024-08-05 15:47:21,562.0,14.0,22.0,13.0,2.0,3.0,17.0,78.0,2019-11-24 16:21:50.000,published-version-V1,1.0,12.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +166,Neural-Network-Models-for-Chemistry,,community,,https://github.com/Eipgen/Neural-Network-Models-for-Chemistry,A collection of Nerual Network Models for chemistry.,11,True,['rep-learn'],Eipgen/Neural-Network-Models-for-Chemistry,https://github.com/Eipgen/Neural-Network-Models-for-Chemistry,2022-05-23 06:35:09,2024-08-13 07:05:05.000,2024-08-08 14:14:24,210.0,12.0,8.0,3.0,22.0,1.0,1.0,65.0,2024-07-17 02:01:45.000,0.0.5,5.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +167,hippynn,,rep-learn,https://github.com/lanl/hippynn/blob/main/LICENSE.txt,https://github.com/lanl/hippynn,python library for atomistic machine learning.,11,True,['workflows'],lanl/hippynn,https://github.com/lanl/hippynn,2021-11-17 00:45:13,2024-08-14 20:00:55.000,2024-08-14 19:09:04,147.0,18.0,23.0,9.0,75.0,6.0,9.0,65.0,2024-01-29 22:04:53.000,hippynn-0.0.3,3.0,14.0,,,1.0,1.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +168,Pacemaker,,ml-iap,https://github.com/ICAMS/python-ace/blob/master/LICENSE.md,https://cortner.github.io/ACEweb/software/,Python package for fitting atomic cluster expansion (ACE) potentials.,11,True,,ICAMS/python-ace,https://github.com/ICAMS/python-ace,2021-11-19 11:39:54,2024-07-22 19:16:32.000,2024-07-22 19:16:32,160.0,1.0,15.0,4.0,24.0,16.0,33.0,62.0,2022-10-24 21:50:17.233,0.2.8,2.0,5.0,python-ace,,,,https://pypi.org/project/python-ace,2022-10-24 21:50:17.233,,17.0,17.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +169,AMPtorch,,general-tool,GPL-3.0,https://github.com/ulissigroup/amptorch,AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch.,11,False,,ulissigroup/amptorch,https://github.com/ulissigroup/amptorch,2019-01-24 15:15:48,2023-07-16 02:11:38.000,2023-07-16 02:08:13,759.0,,32.0,10.0,99.0,7.0,26.0,59.0,2023-07-16 02:11:38.000,1.0,3.0,14.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +170,SIMPLE-NN,,ml-iap,GPL-3.0,https://github.com/MDIL-SNU/SIMPLE-NN,SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE version Neural Network).,11,False,,MDIL-SNU/SIMPLE-NN,https://github.com/MDIL-SNU/SIMPLE-NN,2018-03-26 23:53:35,2022-01-27 05:04:05.000,2022-01-27 05:04:05,586.0,,19.0,12.0,91.0,4.0,26.0,47.0,2021-09-23 01:41:42.000,1.1.1,9.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +171,DeepErwin,,ml-wft,https://github.com/mdsunivie/deeperwin/blob/master/LICENSE,https://github.com/mdsunivie/deeperwin,DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions..,11,True,,mdsunivie/deeperwin,https://github.com/mdsunivie/deeperwin,2021-06-14 15:18:32,2024-06-07 15:52:47.000,2024-06-07 15:52:33,66.0,6.0,6.0,3.0,5.0,,11.0,46.0,2024-03-25 13:47:47.000,transferable_atomic_orbitals,6.0,7.0,deeperwin,,,,https://pypi.org/project/deeperwin,2021-12-14 11:03:19.657,,61.0,61.0,,,,3.0,,,,,,8.0,,,,,,,,,,,,,, +172,nlcc,,language-models,MIT,https://github.com/whitead/nlcc,Natural language computational chemistry command line interface.,11,False,['single-paper'],whitead/nlcc,https://github.com/whitead/nlcc,2021-08-19 18:23:52,2023-02-04 03:07:56.000,2023-02-04 03:06:33,144.0,,7.0,5.0,1.0,,9.0,44.0,2023-02-04 03:11:01.949,0.6.0,10.0,3.0,nlcc,,1.0,1.0,https://pypi.org/project/nlcc,2022-12-07 05:07:49.878,,69.0,69.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +173,pair_nequip,,md,MIT,https://github.com/mir-group/pair_nequip,LAMMPS pair style for NequIP.,11,True,"['ml-iap', 'rep-learn']",mir-group/pair_nequip,https://github.com/mir-group/pair_nequip,2021-04-02 15:28:02,2024-06-05 17:06:39.000,2024-06-05 17:06:39,101.0,4.0,12.0,9.0,8.0,9.0,20.0,41.0,2022-05-20 00:39:04.000,0.5.2,4.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +174,cmlkit,,rep-eng,MIT,https://github.com/sirmarcel/cmlkit,tools for machine learning in condensed matter physics and quantum chemistry.,11,False,['benchmarking'],sirmarcel/cmlkit,https://github.com/sirmarcel/cmlkit,2018-05-31 07:56:52,2022-04-01 00:39:14.000,2022-03-25 22:27:04,526.0,,6.0,3.0,1.0,6.0,2.0,34.0,,,25.0,1.0,cmlkit,,6.0,5.0,https://pypi.org/project/cmlkit,2022-03-25 22:27:16.000,1.0,176.0,176.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +175,NeuralXC,,ml-dft,BSD-3-Clause,https://github.com/semodi/neuralxc,Implementation of a machine learned density functional.,11,False,,semodi/neuralxc,https://github.com/semodi/neuralxc,2019-03-14 18:13:40,2024-06-17 22:55:40.000,2021-07-05 21:36:23,337.0,,10.0,5.0,10.0,5.0,5.0,33.0,,,3.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +176,CBFV,,rep-eng,,https://github.com/Kaaiian/CBFV,Tool to quickly create a composition-based feature vector.,11,False,,kaaiian/CBFV,https://github.com/Kaaiian/CBFV,2019-09-05 23:07:46,2022-03-30 05:47:53.000,2021-10-24 17:10:17,49.0,,6.0,4.0,7.0,5.0,5.0,23.0,2021-10-24 17:22:06.000,1.1.0,3.0,3.0,CBFV,,9.0,9.0,https://pypi.org/project/CBFV,2021-10-24 17:22:06.000,,245.0,245.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +177,BenchML,,rep-eng,Apache-2.0,https://github.com/capoe/benchml,ML benchmarking and pipeling framework.,11,False,['benchmarking'],capoe/benchml,https://github.com/capoe/benchml,2020-04-28 13:26:29,2023-05-24 15:13:06.000,2023-05-24 15:04:57,341.0,,4.0,5.0,7.0,3.0,10.0,15.0,2022-07-14 08:49:29.365,0.3.4,3.0,9.0,benchml,,,,https://pypi.org/project/benchml,2022-07-14 08:49:29.365,,96.0,96.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +178,calorine,,ml-iap,https://gitlab.com/materials-modeling/calorine/-/blob/master/LICENSE,https://calorine.materialsmodeling.org/,A Python package for constructing and sampling neuroevolution potential models. https://doi.org/10.21105/joss.06264.,11,True,,,,2021-04-23 16:12:56,2024-07-26 09:35:09.000,,,,4.0,,,9.0,76.0,12.0,2024-02-28 15:53:18.000,2.2.1,14.0,,calorine,,2.0,,https://pypi.org/project/calorine,2024-07-26 09:35:09.000,2.0,1231.0,1231.0,,,,2.0,,,,,,,,,,,,,,,,,materials-modeling/calorine,https://gitlab.com/materials-modeling/calorine,, +179,CCS_fit,,ml-iap,GPL-3.0,https://github.com/Teoroo-CMC/CCS,Curvature Constrained Splines.,11,True,,Teoroo-CMC/CCS,https://github.com/Teoroo-CMC/CCS,2021-12-13 14:29:53,2024-05-14 08:53:09.000,2024-02-16 09:31:25,762.0,,11.0,3.0,13.0,8.0,6.0,8.0,2024-02-16 09:31:34.000,0.22.5,100.0,8.0,ccs_fit,,,,https://pypi.org/project/ccs_fit,2024-02-16 09:31:34.000,,408.0,428.0,,,,2.0,,,,,,424.0,,,,,,,,,,,,,, +180,pretrained-gnns,,rep-learn,MIT,https://github.com/snap-stanford/pretrain-gnns,Strategies for Pre-training Graph Neural Networks.,10,False,['pretrained'],snap-stanford/pretrain-gnns,https://github.com/snap-stanford/pretrain-gnns,2020-01-30 22:12:41,2023-07-29 06:21:39.000,2023-07-29 06:21:39,13.0,,160.0,17.0,8.0,34.0,29.0,954.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +181,GNoME Explorer,,community,Apache-2.0,https://next-gen.materialsproject.org/materials/gnome,Graph Networks for Materials Exploration Database.,10,True,"['datasets', 'materials-discovery']",google-deepmind/materials_discovery,https://github.com/google-deepmind/materials_discovery,2023-11-28 10:29:51,2024-07-09 19:00:36.000,2023-12-02 03:54:29,8.0,,133.0,47.0,7.0,18.0,4.0,858.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +182,Materials Discovery: GNoME,,materials-discovery,Apache-2.0,https://github.com/google-deepmind/materials_discovery,"Graph Networks for Materials Science (GNoME) and dataset of 381,000 novel stable materials.",10,True,"['uip', 'datasets', 'rep-learn', 'proprietary']",google-deepmind/materials_discovery,https://github.com/google-deepmind/materials_discovery,2023-11-28 10:29:51,2024-07-09 19:00:36.000,2023-12-02 03:54:29,8.0,,133.0,47.0,7.0,18.0,4.0,858.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +183,OpenChem,,general-tool,MIT,https://github.com/Mariewelt/OpenChem,OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research.,10,False,,Mariewelt/OpenChem,https://github.com/Mariewelt/OpenChem,2018-07-10 01:27:33,2023-11-26 05:03:36.000,2022-04-27 19:27:40,444.0,,111.0,36.0,12.0,15.0,2.0,667.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +184,Awesome Materials Informatics,,community,https://github.com/tilde-lab/awesome-materials-informatics#license,https://github.com/tilde-lab/awesome-materials-informatics,"Curated list of known efforts in materials informatics, i.e. in modern materials science.",10,True,,tilde-lab/awesome-materials-informatics,https://github.com/tilde-lab/awesome-materials-informatics,2018-02-15 15:14:16,2024-07-12 09:19:42.000,2024-07-12 09:19:42,138.0,3.0,80.0,17.0,54.0,,8.0,359.0,2023-03-02 19:56:59.000,2023.03.02,1.0,19.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +185,Allegro,,ml-iap,MIT,https://github.com/mir-group/allegro,Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic..,10,False,,mir-group/allegro,https://github.com/mir-group/allegro,2022-02-06 23:50:40,2024-07-01 20:43:10.000,2023-05-08 21:16:45,38.0,,44.0,20.0,5.0,18.0,15.0,310.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +186,GDC,,rep-learn,MIT,https://github.com/gasteigerjo/gdc,"Graph Diffusion Convolution, as proposed in Diffusion Improves Graph Learning (NeurIPS 2019).",10,False,['generative'],gasteigerjo/gdc,https://github.com/gasteigerjo/gdc,2019-10-26 16:05:11,2023-04-26 14:22:40.000,2023-04-26 14:22:40,28.0,,42.0,3.0,1.0,1.0,11.0,262.0,,,,3.0,,,1.0,1.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +187,AI4Chemistry course,,educational,MIT,https://github.com/schwallergroup/ai4chem_course,"EPFL AI for chemistry course, Spring 2023. https://schwallergroup.github.io/ai4chem_course.",10,True,['chemistry'],schwallergroup/ai4chem_course,https://github.com/schwallergroup/ai4chem_course,2022-08-22 07:29:30,2024-05-02 20:41:12.000,2024-05-02 20:41:12,232.0,,30.0,4.0,9.0,1.0,3.0,127.0,,,,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +188,DeePKS-kit,,ml-dft,LGPL-3.0,https://github.com/deepmodeling/deepks-kit,a package for developing machine learning-based chemically accurate energy and density functional models.,10,True,,deepmodeling/deepks-kit,https://github.com/deepmodeling/deepks-kit,2020-07-29 03:27:50,2024-08-08 16:28:48.000,2024-04-13 03:44:40,384.0,,35.0,14.0,44.0,5.0,14.0,96.0,,,,7.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +189,Grad DFT,,ml-dft,Apache-2.0,https://github.com/XanaduAI/GradDFT,GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation..,10,True,,XanaduAI/GradDFT,https://github.com/XanaduAI/GradDFT,2023-05-15 16:18:25,2024-02-13 16:05:53.000,2024-02-13 16:05:51,419.0,,5.0,5.0,43.0,11.0,43.0,71.0,,,,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +190,LLaMP,,language-models,BSD-3-Clause,https://github.com/chiang-yuan/llamp,A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials informatics. An..,10,True,"['materials-discovery', 'cheminformatics', 'generative', 'MD', 'multimodal', 'language-models', 'lang-py', 'general-tool']",chiang-yuan/llamp,https://github.com/chiang-yuan/llamp,2023-07-01 08:15:34,2024-08-11 08:26:47.000,2024-08-01 23:06:38,372.0,7.0,7.0,,30.0,8.0,17.0,52.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +191,DSECOP,,educational,CCO-1.0,https://github.com/GDS-Education-Community-of-Practice/DSECOP,This repository contains data science educational materials developed by DSECOP Fellows.,10,True,,GDS-Education-Community-of-Practice/DSECOP,https://github.com/GDS-Education-Community-of-Practice/DSECOP,2022-03-07 17:47:33,2024-06-26 14:49:22.000,2024-06-26 14:49:19,555.0,6.0,25.0,10.0,25.0,1.0,7.0,43.0,,,,13.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +192,Finetuna,,active-learning,MIT,https://github.com/ulissigroup/finetuna,Active Learning for Machine Learning Potentials.,10,True,,ulissigroup/finetuna,https://github.com/ulissigroup/finetuna,2020-09-22 14:39:52,2024-05-15 17:26:24.000,2024-05-15 17:25:23,1200.0,,11.0,3.0,40.0,5.0,15.0,42.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +193,Atom2Vec,,rep-learn,MIT,https://github.com/idocx/Atom2Vec,Atom2Vec: a simple way to describe atoms for machine learning.,10,True,,idocx/Atom2Vec,https://github.com/idocx/Atom2Vec,2020-01-18 23:31:47,2024-02-23 21:44:03.000,2024-02-23 21:43:58,4.0,,9.0,1.0,1.0,2.0,1.0,32.0,2024-02-23 21:43:41.000,1.1.0,2.0,1.0,atom2vec,,2.0,2.0,https://pypi.org/project/atom2vec,2024-02-23 21:43:41.000,,69.0,69.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +194,OpenKIM,,datasets,LGPL-2.1,https://openkim.org/,"The Open Knowledgebase of Interatomic Models (OpenKIM) aims to be an online resource for standardized testing, long-..",10,False,"['model-repository', 'knowledge-base', 'pretrained']",openkim/kim-api,https://github.com/openkim/kim-api,2014-07-28 21:21:08,2023-08-16 00:09:44.000,2022-03-17 23:01:36,2371.0,,20.0,12.0,55.0,17.0,18.0,31.0,,,,24.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +195,FAENet,,rep-learn,MIT,https://github.com/vict0rsch/faenet,Frame Averaging Equivariant GNN for materials modeling.,10,True,,vict0rsch/faenet,https://github.com/vict0rsch/faenet,2023-02-10 22:10:27,2023-10-12 08:46:26.000,2023-10-12 08:46:22,125.0,,2.0,3.0,5.0,,,25.0,2023-09-12 04:00:49.000,0.1.2,3.0,3.0,faenet,,2.0,2.0,https://pypi.org/project/faenet,2023-09-14 21:06:36.000,,77.0,77.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +196,Point Edge Transformer (PET),,ml-iap,MIT,https://github.com/spozdn/pet,Point Edge Transformer.,10,True,"['rep-learn', 'transformer']",spozdn/pet,https://github.com/spozdn/pet,2023-02-08 18:36:10,2024-07-18 13:35:00.000,2024-07-02 10:29:58,201.0,10.0,5.0,4.0,12.0,5.0,,18.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +197,ACEhamiltonians,,ml-dft,MIT,https://github.com/ACEsuit/ACEhamiltonians.jl,"Provides tools for constructing, fitting, and predicting self-consistent Hamiltonian and overlap matrices in solid-..",10,False,['lang-julia'],ACEsuit/ACEhamiltonians.jl,https://github.com/ACEsuit/ACEhamiltonians.jl,2022-01-17 20:54:22,2024-06-05 15:25:30.000,2023-04-12 15:04:14,33.0,,6.0,5.0,42.0,2.0,3.0,12.0,2024-02-07 16:35:47.000,0.1.0,2.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +198,SiMGen,,generative,MIT,https://github.com/RokasEl/simgen,Zero Shot Molecular Generation via Similarity Kernels.,10,True,['visualization'],RokasEl/simgen,https://github.com/RokasEl/simgen,2023-01-25 16:41:18,2024-06-13 15:43:18.000,2024-02-15 10:31:41,257.0,,,2.0,23.0,1.0,3.0,11.0,2024-02-14 10:35:02.000,0.1.0,2.0,4.0,simgen,,1.0,1.0,https://pypi.org/project/simgen,2024-02-14 11:08:25.000,,34.0,34.0,,,,2.0,,,,,,,,,,,,,,,,,,,, +199,NNsforMD,,ml-iap,MIT,https://github.com/aimat-lab/NNsForMD,"Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings.",10,False,,aimat-lab/NNsForMD,https://github.com/aimat-lab/NNsForMD,2020-08-31 11:14:18,2022-11-10 13:04:49.000,2022-11-10 13:04:45,265.0,,6.0,3.0,,,,10.0,2022-04-12 15:15:00.183,2.0.0,5.0,2.0,pyNNsMD,,1.0,1.0,https://pypi.org/project/pyNNsMD,2022-04-12 15:15:00.183,,50.0,50.0,,,,3.0,,,,,,,,,,,,,,,,,,,, +200,ACEfit,,ml-iap,MIT,https://github.com/ACEsuit/ACEfit.jl,,10,True,['lang-julia'],ACEsuit/ACEfit.jl,https://github.com/ACEsuit/ACEfit.jl,2022-01-01 00:09:17,2024-08-13 04:26:08.000,2024-08-13 04:22:01,226.0,5.0,5.0,4.0,22.0,22.0,33.0,7.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +201,SE(3)-Transformers,,rep-learn,MIT,https://github.com/FabianFuchsML/se3-transformer-public,code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503.,9,False,"['single-paper', 'transformer']",FabianFuchsML/se3-transformer-public,https://github.com/FabianFuchsML/se3-transformer-public,2020-08-31 10:36:57,2023-07-10 05:13:25.000,2021-11-18 09:11:56,63.0,,67.0,17.0,5.0,11.0,17.0,482.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +202,EDM,,generative,MIT,https://github.com/ehoogeboom/e3_diffusion_for_molecules,E(3) Equivariant Diffusion Model for Molecule Generation in 3D.,9,False,,ehoogeboom/e3_diffusion_for_molecules,https://github.com/ehoogeboom/e3_diffusion_for_molecules,2022-04-15 14:34:35,2022-07-10 17:56:18.000,2022-07-10 17:56:12,6.0,,109.0,8.0,,10.0,36.0,417.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +203,DimeNet,,ml-iap,https://github.com/gasteigerjo/dimenet/blob/master/LICENSE.md,https://github.com/gasteigerjo/dimenet,"DimeNet and DimeNet++ models, as proposed in Directional Message Passing for Molecular Graphs (ICLR 2020) and Fast and..",9,True,,gasteigerjo/dimenet,https://github.com/gasteigerjo/dimenet,2020-02-14 12:40:15,2023-10-03 09:57:19.000,2023-10-03 09:57:19,103.0,,59.0,4.0,,1.0,30.0,280.0,,,,2.0,,,1.0,1.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +204,MoLFormers UI,,community,Apache-2.0,https://molformer.res.ibm.com/,A family of foundation models trained on chemicals.,9,True,"['transformer', 'language-models', 'pretrained', 'drug-discovery']",IBM/molformer,https://github.com/IBM/molformer,2022-11-07 18:48:17,2023-10-16 16:34:25.000,2023-10-16 16:33:13,7.0,,40.0,10.0,3.0,9.0,10.0,231.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +205,MoLFormer,,language-models,Apache-2.0,https://github.com/IBM/molformer,Repository for MolFormer.,9,True,"['transformer', 'pretrained', 'drug-discovery']",IBM/molformer,https://github.com/IBM/molformer,2022-11-07 18:48:17,2023-10-16 16:34:25.000,2023-10-16 16:33:13,7.0,,40.0,10.0,3.0,9.0,10.0,231.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +206,SchNet,,ml-iap,MIT,https://github.com/atomistic-machine-learning/SchNet,SchNet - a deep learning architecture for quantum chemistry.,9,False,,atomistic-machine-learning/SchNet,https://github.com/atomistic-machine-learning/SchNet,2017-10-03 11:52:20,2018-09-04 08:42:35.000,2018-09-04 08:42:34,53.0,,65.0,16.0,,1.0,2.0,212.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +207,Equiformer,,rep-learn,MIT,https://github.com/atomicarchitects/equiformer,[ICLR23 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs.,9,True,['transformer'],atomicarchitects/equiformer,https://github.com/atomicarchitects/equiformer,2023-02-28 00:21:30,2024-07-27 08:42:53.000,2024-07-18 10:32:17,6.0,3.0,36.0,5.0,2.0,5.0,8.0,187.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +208,GemNet,,ml-iap,https://github.com/TUM-DAML/gemnet_pytorch/blob/master/LICENSE,https://github.com/TUM-DAML/gemnet_pytorch,"GemNet model in PyTorch, as proposed in GemNet: Universal Directional Graph Neural Networks for Molecules (NeurIPS..",9,False,,TUM-DAML/gemnet_pytorch,https://github.com/TUM-DAML/gemnet_pytorch,2021-10-11 07:30:36,2023-04-26 14:20:12.000,2023-04-26 14:20:12,36.0,,27.0,4.0,1.0,,14.0,176.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +209,MolSkill,,language-models,MIT,https://github.com/microsoft/molskill,Extracting medicinal chemistry intuition via preference machine learning.,9,True,"['drug-discovery', 'recommender']",microsoft/molskill,https://github.com/microsoft/molskill,2023-01-12 13:48:31,2023-10-31 17:03:36.000,2023-10-31 17:03:36,81.0,,9.0,6.0,8.0,2.0,4.0,101.0,2023-08-04 12:22:15.000,1.2b,5.0,4.0,,msr-ai4science/molskill,,,,,,,15.0,https://anaconda.org/msr-ai4science/molskill,2023-06-18 17:27:43.196,275.0,3.0,,,,,,,,,,,,,,,,,,,, +210,GATGNN: Global Attention Graph Neural Network,,rep-learn,MIT,https://github.com/superlouis/GATGNN,Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials..,9,False,,superlouis/GATGNN,https://github.com/superlouis/GATGNN,2020-06-21 03:27:36,2022-10-03 21:57:33.000,2022-10-03 21:57:33,99.0,,17.0,8.0,,3.0,3.0,67.0,2021-04-05 06:49:29.000,0.2,2.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +211,ACE.jl,,ml-iap,https://github.com/ACEsuit/ACE.jl/blob/main/license/mit.md,https://github.com/ACEsuit/ACE.jl,Parameterisation of Equivariant Properties of Particle Systems.,9,False,['lang-julia'],ACEsuit/ACE.jl,https://github.com/ACEsuit/ACE.jl,2019-11-30 16:22:51,2023-06-09 21:31:30.000,2023-06-09 21:29:10,912.0,,15.0,8.0,65.0,24.0,58.0,65.0,,,,12.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +212,PROPhet,,ml-dft,GPL-3.0,https://github.com/biklooost/PROPhet,PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches.,9,False,"['ml-iap', 'md', 'single-paper', 'lang-cpp']",biklooost/PROPhet,https://github.com/biklooost/PROPhet,2016-09-16 16:21:06,2018-04-19 02:09:46.000,2018-04-19 02:00:46,120.0,,26.0,14.0,6.0,9.0,7.0,62.0,2018-04-15 16:55:15.000,1.2,3.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +213,AI for Science paper collection,,community,Apache-2.0,https://github.com/sherrylixuecheng/AI_for_Science_paper_collection,List the AI for Science papers accepted by top conferences.,9,True,,sherrylixuecheng/AI_for_Science_paper_collection,https://github.com/sherrylixuecheng/AI_for_Science_paper_collection,2024-06-28 16:20:57,2024-08-18 19:18:00.000,2024-08-18 18:03:30,72.0,72.0,5.0,2.0,9.0,,,44.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +214,GAP,,ml-iap,https://github.com/libAtoms/GAP/blob/main/LICENSE.md,https://libatoms.github.io/,Gaussian Approximation Potential (GAP).,9,True,,libAtoms/GAP,https://github.com/libAtoms/GAP,2021-03-22 14:48:56,2024-08-17 08:35:27.000,2024-08-17 08:35:27,206.0,3.0,20.0,10.0,67.0,,,39.0,,,,13.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +215,pair_allegro,,md,MIT,https://github.com/mir-group/pair_allegro,LAMMPS pair style for Allegro deep learning interatomic potentials with parallelization support.,9,True,"['ml-iap', 'rep-learn']",mir-group/pair_allegro,https://github.com/mir-group/pair_allegro,2021-08-09 17:26:51,2024-06-05 17:00:50.000,2024-06-05 17:00:50,101.0,10.0,8.0,10.0,3.0,11.0,18.0,34.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +216,ALF,,ml-iap,https://github.com/lanl/ALF/blob/main/LICENSE,https://github.com/lanl/ALF,A framework for performing active learning for training machine-learned interatomic potentials.,9,True,['active-learning'],lanl/alf,https://github.com/lanl/ALF,2023-01-04 23:13:24,2024-08-08 16:59:38.000,2024-08-08 16:59:10,149.0,3.0,11.0,8.0,27.0,,,29.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +217,lie-nn,,math,MIT,https://github.com/lie-nn/lie-nn,Tools for building equivariant polynomials on reductive Lie groups.,9,False,['rep-learn'],lie-nn/lie-nn,https://github.com/lie-nn/lie-nn,2022-04-01 18:02:49,2023-06-29 19:38:34.000,2023-06-20 22:30:53,249.0,,1.0,8.0,3.0,1.0,,26.0,2023-06-20 22:31:12.000,0.0.0,1.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +218,iam-notebooks,,educational,Apache-2.0,https://github.com/ceriottm/iam-notebooks,Jupyter notebooks for the lectures of the Introduction to Atomistic Modeling.,9,True,,ceriottm/iam-notebooks,https://github.com/ceriottm/iam-notebooks,2020-11-23 21:27:41,2024-06-26 12:42:53.000,2024-06-26 12:42:45,242.0,2.0,5.0,4.0,7.0,4.0,,24.0,,,,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +219,PACE,,md,https://github.com/ICAMS/lammps-user-pace/blob/main/LICENSE,https://github.com/ICAMS/lammps-user-pace,"The LAMMPS ML-IAP `pair_style pace`, aka Atomic Cluster Expansion (ACE), aka ML-PACE,..",9,True,,ICAMS/lammps-user-pace,https://github.com/ICAMS/lammps-user-pace,2021-02-25 10:04:48,2024-07-10 09:17:44.000,2023-11-27 21:28:13,59.0,,10.0,6.0,16.0,2.0,6.0,23.0,2023-11-25 21:58:41.000,.2023.11.25.fix,6.0,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +220,SkipAtom,,rep-eng,MIT,https://github.com/lantunes/skipatom,"Distributed representations of atoms, inspired by the Skip-gram model.",9,False,,lantunes/skipatom,https://github.com/lantunes/skipatom,2021-06-19 13:09:13,2023-07-16 19:28:39.000,2022-05-04 13:18:30,46.0,,3.0,2.0,7.0,3.0,1.0,23.0,2022-05-04 13:20:18.000,1.2.5,12.0,1.0,skipatom,conda-forge/skipatom,1.0,1.0,https://pypi.org/project/skipatom,2022-05-04 13:20:18.000,,143.0,203.0,https://anaconda.org/conda-forge/skipatom,2023-06-18 08:42:05.505,1502.0,3.0,,,,,,,,,,,,,,,,,,,, +221,UVVisML,,rep-learn,MIT,https://github.com/learningmatter-mit/uvvisml,Predict optical properties of molecules with machine learning.,9,False,"['optical-properties', 'single-paper', 'probabilistic']",learningmatter-mit/uvvisml,https://github.com/learningmatter-mit/uvvisml,2021-10-13 05:58:48,2023-05-26 22:35:14.000,2023-05-26 22:35:14,17.0,,6.0,4.0,1.0,,,21.0,2022-02-06 18:14:14.000,0.0.2,2.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +222,TurboGAP,,ml-iap,https://github.com/mcaroba/turbogap/blob/master/LICENSE.md,https://github.com/mcaroba/turbogap,The TurboGAP code.,9,True,['lang-fortran'],mcaroba/turbogap,https://github.com/mcaroba/turbogap,2021-05-02 09:19:05,2024-08-08 08:26:12.000,2024-07-09 06:58:53,309.0,12.0,8.0,8.0,7.0,6.0,3.0,16.0,,,,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +223,OPTIMADE Tutorial Exercises,,educational,MIT,https://github.com/Materials-Consortia/optimade-tutorial-exercises,Tutorial exercises for the OPTIMADE API.,9,True,['datasets'],Materials-Consortia/optimade-tutorial-exercises,https://github.com/Materials-Consortia/optimade-tutorial-exercises,2021-08-25 17:33:15,2023-09-27 08:32:31.000,2023-09-27 08:32:30,49.0,,7.0,12.0,15.0,,3.0,14.0,2023-06-12 07:47:14.000,2.0.1,5.0,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +224,Q-stack,,ml-dft,MIT,https://github.com/lcmd-epfl/Q-stack,Stack of codes for dedicated pre- and post-processing tasks for Quantum Machine Learning (QML).,9,True,"['excited-states', 'general-tool']",lcmd-epfl/Q-stack,https://github.com/lcmd-epfl/Q-stack,2021-10-20 15:33:26,2024-07-25 14:47:23.000,2024-07-19 11:29:08,413.0,32.0,5.0,2.0,45.0,9.0,20.0,14.0,,,1.0,7.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +225,AGOX,,materials-discovery,GPL-3.0,https://agox.gitlab.io/agox/,AGOX is a package for global optimization of atomic system using e.g. the energy calculated from density functional..,9,True,['structure-optimization'],,,2022-03-08 09:08:13,2024-08-03 18:26:35.000,,,,5.0,,,14.0,9.0,13.0,2023-08-31 12:33:38.000,3.1.1,4.0,,agox,,,,https://pypi.org/project/agox,2024-08-03 18:26:35.000,,211.0,211.0,,,,2.0,,,,,,,,,,,,,,,,,agox/agox,https://gitlab.com/agox/agox,, +226,Materials Data Facility (MDF),,datasets,Apache-2.0,https://www.materialsdatafacility.org,"A simple way to publish, discover, and access materials datasets. Publication of very large datasets supported (e.g.,..",9,True,,materials-data-facility/connect_client,https://github.com/materials-data-facility/connect_client,2018-09-12 20:49:58,2024-03-10 03:11:45.000,2024-02-05 22:48:40,158.0,,1.0,4.0,35.0,1.0,6.0,10.0,2024-02-05 22:49:58.000,0.5.0,23.0,7.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +227,optimade.science,,community,MIT,https://optimade.science,A sky-scanner Optimade browser-only GUI.,9,True,['datasets'],tilde-lab/optimade.science,https://github.com/tilde-lab/optimade.science,2019-06-08 14:10:54,2024-06-10 12:03:39.000,2024-06-10 12:03:39,247.0,4.0,2.0,4.0,32.0,7.0,19.0,8.0,2023-03-02 20:13:25.000,2.0.0,1.0,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +228,2DMD dataset,,datasets,Apache-2.0,https://github.com/HSE-LAMBDA/ai4material_design/blob/main/docs/DATA.md,"Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of..",9,True,['material-defect'],HSE-LAMBDA/ai4material_design,https://github.com/HSE-LAMBDA/ai4material_design,2021-03-25 10:06:20,2023-11-21 11:30:42.000,2023-11-21 11:30:33,1118.0,,3.0,8.0,28.0,,12.0,6.0,,,,11.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +229,ai4material_design,,rep-learn,Apache-2.0,https://github.com/HSE-LAMBDA/ai4material_design,"Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of..",9,True,"['pretrained', 'material-defect']",HSE-LAMBDA/ai4material_design,https://github.com/HSE-LAMBDA/ai4material_design,2021-03-25 10:06:20,2023-11-21 11:30:42.000,2023-11-21 11:30:33,1118.0,,3.0,8.0,28.0,,12.0,6.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +230,MADICES Awesome Interoperability,,community,MIT,MADICES/MADICES.github.io/blob/main/docs/awesome_interoperability.md,Linked data interoperability resources of the Machine-actionable data interoperability for the chemical sciences..,9,False,['datasets'],MADICES/MADICES.github.io,https://github.com/MADICES/MADICES.github.io,2021-12-26 13:27:32,2024-07-10 07:36:51.000,2024-07-10 07:35:07,217.0,18.0,5.0,4.0,17.0,1.0,14.0,1.0,,,,10.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +231,Awesome Neural Geometry,,community,,https://github.com/neurreps/awesome-neural-geometry,"A curated collection of resources and research related to the geometry of representations in the brain, deep networks,..",8,True,"['educational', 'rep-learn']",neurreps/awesome-neural-geometry,https://github.com/neurreps/awesome-neural-geometry,2022-07-31 01:19:57,2024-08-13 14:39:08.000,2024-07-17 18:47:49,124.0,2.0,56.0,28.0,13.0,,1.0,887.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +232,molecularGNN_smiles,,rep-learn,Apache-2.0,https://github.com/masashitsubaki/molecularGNN_smiles,"The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius..",8,False,,masashitsubaki/molecularGNN_smiles,https://github.com/masashitsubaki/molecularGNN_smiles,2018-11-06 00:25:26,2020-11-28 02:04:45.000,2020-11-28 02:04:45,79.0,,75.0,6.0,,6.0,1.0,287.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +233,Awesome-Graph-Generation,,community,,https://github.com/yuanqidu/awesome-graph-generation,A curated list of up-to-date graph generation papers and resources.,8,True,['rep-learn'],yuanqidu/awesome-graph-generation,https://github.com/yuanqidu/awesome-graph-generation,2021-08-07 05:43:46,2024-06-24 01:56:37.000,2024-03-17 06:07:46,84.0,,17.0,7.0,2.0,,,255.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +234,RDKit Tutorials,,educational,https://github.com/rdkit/rdkit-tutorials/blob/master/LICENSE,https://github.com/rdkit/rdkit-tutorials,Tutorials to learn how to work with the RDKit.,8,False,,rdkit/rdkit-tutorials,https://github.com/rdkit/rdkit-tutorials,2016-10-07 03:34:01,2023-03-19 13:36:55.000,2023-03-19 13:36:55,68.0,,70.0,17.0,7.0,5.0,1.0,251.0,,,,5.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +235,QDF for molecule,,ml-esm,MIT,https://github.com/masashitsubaki/QuantumDeepField_molecule,"Quantum deep field: data-driven wave function, electron density generation, and energy prediction and extrapolation..",8,False,,masashitsubaki/QuantumDeepField_molecule,https://github.com/masashitsubaki/QuantumDeepField_molecule,2020-11-11 01:06:09,2021-02-20 03:46:18.000,2021-02-20 03:46:09,20.0,,40.0,4.0,,1.0,3.0,197.0,,,,1.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,, +236,EquiformerV2,,rep-learn,MIT,https://github.com/atomicarchitects/equiformer_v2,[ICLR24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations.,8,True,,atomicarchitects/equiformer_v2,https://github.com/atomicarchitects/equiformer_v2,2023-06-21 07:09:58,2024-07-31 23:38:48.000,2024-07-16 05:51:23,16.0,4.0,25.0,5.0,1.0,15.0,2.0,185.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +237,BestPractices,,educational,MIT,https://github.com/anthony-wang/BestPractices,Things that you should (and should not) do in your Materials Informatics research.,8,True,,anthony-wang/BestPractices,https://github.com/anthony-wang/BestPractices,2020-05-05 19:41:25,2023-11-17 02:58:25.000,2023-11-17 02:58:25,17.0,,68.0,8.0,8.0,5.0,2.0,165.0,,,,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +238,G-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/G-SchNet,G-SchNet - a generative model for 3d molecular structures.,8,False,,atomistic-machine-learning/G-SchNet,https://github.com/atomistic-machine-learning/G-SchNet,2019-10-21 13:48:59,2023-03-24 12:05:41.000,2023-03-24 12:05:41,64.0,,25.0,7.0,,,10.0,129.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +239,ANI-1 Dataset,,datasets,MIT,https://github.com/isayev/ANI1_dataset,A data set of 20 million calculated off-equilibrium conformations for organic molecules.,8,False,,isayev/ANI1_dataset,https://github.com/isayev/ANI1_dataset,2017-08-07 20:08:46,2022-08-08 15:56:17.000,2022-08-08 15:56:17,25.0,,18.0,12.0,2.0,8.0,3.0,96.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +240,AIMNet,,ml-iap,MIT,https://github.com/aiqm/aimnet,Atoms In Molecules Neural Network Potential.,8,False,['single-paper'],aiqm/aimnet,https://github.com/aiqm/aimnet,2018-09-26 17:28:37,2019-11-21 23:49:01.000,2019-11-21 23:49:00,7.0,,24.0,10.0,2.0,4.0,,94.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +241,MoleculeNet Leaderboard,,datasets,MIT,https://github.com/deepchem/moleculenet,,8,False,['benchmarking'],deepchem/moleculenet,https://github.com/deepchem/moleculenet,2020-02-24 18:14:05,2021-04-29 19:51:06.000,2021-04-29 19:51:06,78.0,,19.0,5.0,15.0,24.0,5.0,87.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +242,Awesome Neural SBI,,community,MIT,https://github.com/smsharma/awesome-neural-sbi,Community-sourced list of papers and resources on neural simulation-based inference.,8,True,['active-learning'],smsharma/awesome-neural-sbi,https://github.com/smsharma/awesome-neural-sbi,2023-01-20 19:48:13,2024-06-17 04:24:32.000,2024-06-17 04:24:27,56.0,5.0,4.0,6.0,2.0,1.0,1.0,82.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +243,MACE-Jax,,ml-iap,MIT,https://github.com/ACEsuit/mace-jax,Equivariant machine learning interatomic potentials in JAX.,8,True,,ACEsuit/mace-jax,https://github.com/ACEsuit/mace-jax,2023-02-06 12:10:16,2023-10-04 08:07:35.000,2023-10-04 08:07:35,207.0,,5.0,11.0,1.0,3.0,3.0,58.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +244,graphite,,rep-learn,MIT,https://github.com/LLNL/graphite,A repository for implementing graph network models based on atomic structures.,8,True,,llnl/graphite,https://github.com/LLNL/graphite,2022-06-27 19:15:27,2024-08-08 04:10:45.000,2024-08-08 04:10:44,30.0,2.0,9.0,5.0,4.0,2.0,1.0,53.0,,,,2.0,,,11.0,11.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +245,HamGNN,,ml-dft,GPL-3.0,https://github.com/QuantumLab-ZY/HamGNN,An E(3) equivariant Graph Neural Network for predicting electronic Hamiltonian matrix.,8,True,"['rep-learn', 'magnetism', 'lang-c']",QuantumLab-ZY/HamGNN,https://github.com/QuantumLab-ZY/HamGNN,2023-07-14 12:20:27,2024-08-06 17:17:12.000,2024-08-06 17:16:48,63.0,22.0,12.0,5.0,,20.0,4.0,50.0,,,,1.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +246,DeeperGATGNN,,rep-learn,MIT,https://github.com/usccolumbia/deeperGATGNN,Scalable graph neural networks for materials property prediction.,8,True,,usccolumbia/deeperGATGNN,https://github.com/usccolumbia/deeperGATGNN,2021-09-29 17:31:02,2024-01-19 18:11:52.000,2024-01-19 18:11:38,25.0,,8.0,3.0,1.0,4.0,8.0,46.0,2022-03-08 02:14:28.000,1.0,1.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +247,Sketchmap,,unsupervised,GPL-3.0,https://github.com/lab-cosmo/sketchmap,Suite of programs to perform non-linear dimensionality reduction -- sketch-map in particular.,8,False,['lang-cpp'],lab-cosmo/sketchmap,https://github.com/lab-cosmo/sketchmap,2014-05-20 09:33:32,2024-02-20 20:57:41.000,2023-05-24 22:47:50,64.0,,10.0,31.0,1.0,4.0,5.0,44.0,,,,8.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +248,PyNEP,,ml-iap,MIT,https://github.com/bigd4/PyNEP,A python interface of the machine learning potential NEP used in GPUMD.,8,True,,bigd4/PyNEP,https://github.com/bigd4/PyNEP,2022-03-21 06:27:13,2024-06-01 09:06:22.000,2024-06-01 09:06:22,80.0,3.0,17.0,2.0,15.0,4.0,7.0,44.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +249,SIMPLE-NN v2,,ml-iap,GPL-3.0,https://github.com/MDIL-SNU/SIMPLE-NN_v2,SIMPLE-NN is an open package that constructs Behler-Parrinello-type neural-network interatomic potentials from ab..,8,True,,MDIL-SNU/SIMPLE-NN_v2,https://github.com/MDIL-SNU/SIMPLE-NN_v2,2021-03-02 09:36:49,2023-12-29 02:08:47.000,2023-12-29 02:08:47,504.0,,17.0,5.0,88.0,4.0,9.0,39.0,,,,13.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +250,SNAP,,ml-iap,BSD-3-Clause,https://github.com/materialsvirtuallab/snap,Repository for spectral neighbor analysis potential (SNAP) model development.,8,False,,materialsvirtuallab/snap,https://github.com/materialsvirtuallab/snap,2017-06-26 21:56:00,2020-06-30 05:20:37.000,2020-06-30 05:20:37,38.0,,17.0,11.0,1.0,1.0,3.0,36.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +251,Atomistic Adversarial Attacks,,ml-iap,MIT,https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks,Code for performing adversarial attacks on atomistic systems using NN potentials.,8,False,['probabilistic'],learningmatter-mit/Atomistic-Adversarial-Attacks,https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks,2021-03-28 17:39:52,2022-10-03 16:19:31.000,2022-10-03 16:19:29,33.0,,7.0,5.0,1.0,,1.0,30.0,2021-07-19 18:09:36.000,1.0.1,1.0,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +252,CGAT,,rep-learn,MIT,https://github.com/hyllios/CGAT,Crystal graph attention neural networks for materials prediction.,8,False,,hyllios/CGAT,https://github.com/hyllios/CGAT,2021-03-28 09:51:15,2023-07-18 12:04:35.000,2023-01-10 22:31:07,153.0,,8.0,3.0,1.0,,1.0,25.0,2023-07-18 12:04:35.000,0.1,1.0,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +253,ACE1.jl,,ml-iap,https://github.com/ACEsuit/ACE1.jl/blob/main/ASL.md,https://acesuit.github.io/,Atomic Cluster Expansion for Modelling Invariant Atomic Properties.,8,True,['lang-julia'],ACEsuit/ACE1.jl,https://github.com/ACEsuit/ACE1.jl,2022-01-14 19:52:49,2024-07-02 14:12:25.000,2024-07-02 14:12:23,558.0,1.0,7.0,5.0,30.0,22.0,24.0,20.0,,,,9.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +254,GElib,,math,MPL-2.0,https://github.com/risi-kondor/GElib,C++/CUDA library for SO(3) equivariant operations.,8,True,['lang-cpp'],risi-kondor/GElib,https://github.com/risi-kondor/GElib,2021-08-24 20:56:40,2024-08-06 20:45:38.000,2024-07-27 21:36:58,602.0,1.0,3.0,4.0,4.0,3.0,4.0,19.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +255,bVAE-IM,,generative,MIT,https://github.com/tsudalab/bVAE-IM,Implementation of Chemical Design with GPU-based Ising Machine.,8,False,"['qml', 'single-paper']",tsudalab/bVAE-IM,https://github.com/tsudalab/bVAE-IM,2023-03-01 08:26:56,2023-07-11 04:39:24.000,2023-07-11 04:39:24,39.0,,3.0,8.0,,,,11.0,2023-03-01 14:26:13.000,1.0.0,1.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +256,T-e3nn,,rep-learn,MIT,https://github.com/Hongyu-yu/T-e3nn,Time-reversal Euclidean neural networks based on e3nn.,8,False,['magnetism'],Hongyu-yu/T-e3nn,https://github.com/Hongyu-yu/T-e3nn,2022-11-21 14:49:45,2023-02-21 16:36:26.000,2023-02-21 16:36:25,2145.0,,,2.0,,,,8.0,,,,26.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +257,MLXDM,,ml-iap,MIT,https://github.com/RowleyGroup/MLXDM,A Neural Network Potential with Rigorous Treatment of Long-Range Dispersion https://doi.org/10.1039/D2DD00150K.,8,True,['long-range'],RowleyGroup/MLXDM,https://github.com/RowleyGroup/MLXDM,2022-05-03 17:47:26,2024-08-15 21:32:50.000,2024-08-15 21:32:12,53.0,18.0,2.0,5.0,,,,6.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +258,MEGNetSparse,,ml-iap,MIT,https://github.com/HSE-LAMBDA/MEGNetSparse,"A library imlementing a graph neural network with sparse representation from Code for Kazeev, N., Al-Maeeni, A.R.,..",8,False,['material-defect'],HSE-LAMBDA/MEGNetSparse,https://github.com/HSE-LAMBDA/MEGNetSparse,2023-07-19 08:17:42,2023-08-21 17:11:34.000,2023-08-21 17:11:25,19.0,,1.0,2.0,,,,1.0,2023-08-21 17:11:01.000,0.0.10,9.0,2.0,MEGNetSparse,,1.0,1.0,https://pypi.org/project/MEGNetSparse,2023-08-21 17:11:01.000,,51.0,51.0,,,,3.0,,,,,,,,,,,,,,,,,,,, +259,GEOM,,datasets,,https://github.com/learningmatter-mit/geom,GEOM: Energy-annotated molecular conformations.,7,False,['drug-discovery'],learningmatter-mit/geom,https://github.com/learningmatter-mit/geom,2020-06-03 17:58:37,2022-04-24 18:57:39.000,2022-04-24 18:57:39,95.0,,24.0,10.0,,2.0,11.0,189.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +260,tensorfieldnetworks,,rep-learn,MIT,https://github.com/tensorfieldnetworks/tensorfieldnetworks,Rotation- and translation-equivariant neural networks for 3D point clouds.,7,False,,tensorfieldnetworks/tensorfieldnetworks,https://github.com/tensorfieldnetworks/tensorfieldnetworks,2018-02-09 23:18:13,2020-01-07 17:22:16.000,2020-01-07 17:22:15,10.0,,28.0,9.0,2.0,1.0,2.0,151.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +261,A Highly Opinionated List of Open-Source Materials Informatics Resources,,community,MIT,https://github.com/ncfrey/resources,A Highly Opinionated List of Open Source Materials Informatics Resources.,7,False,,ncfrey/resources,https://github.com/ncfrey/resources,2020-11-17 23:47:07,2022-02-18 13:37:51.000,2022-02-18 13:37:51,8.0,,22.0,9.0,,,,116.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +262,PhysNet,,ml-iap,MIT,https://github.com/MMunibas/PhysNet,Code for training PhysNet models.,7,False,['electrostatics'],MMunibas/PhysNet,https://github.com/MMunibas/PhysNet,2019-03-28 09:05:22,2022-10-16 17:45:42.000,2020-12-07 11:09:20,4.0,,26.0,9.0,1.0,5.0,,88.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +263,DTNN,,rep-learn,MIT,https://github.com/atomistic-machine-learning/dtnn,Deep Tensor Neural Network.,7,False,,atomistic-machine-learning/dtnn,https://github.com/atomistic-machine-learning/dtnn,2017-03-10 14:40:05,2017-07-11 08:26:15.000,2017-07-11 08:25:39,9.0,,31.0,14.0,,,3.0,76.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +264,JAXChem,,general-tool,MIT,https://github.com/deepchem/jaxchem,JAXChem is a JAX-based deep learning library for complex and versatile chemical modeling.,7,False,,deepchem/jaxchem,https://github.com/deepchem/jaxchem,2020-05-11 18:54:41,2020-07-15 05:02:21.000,2020-07-15 04:55:41,96.0,,10.0,7.0,13.0,1.0,1.0,76.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +265,Cormorant,,rep-learn,https://github.com/risilab/cormorant/blob/master/LICENSE,https://github.com/risilab/cormorant,Codebase for Cormorant Neural Networks.,7,False,,risilab/cormorant,https://github.com/risilab/cormorant,2019-10-27 18:22:07,2022-05-11 12:49:05.000,2020-03-11 15:25:51,160.0,,10.0,6.0,1.0,3.0,,59.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +266,Awesome-Crystal-GNNs,,community,MIT,https://github.com/kdmsit/Awesome-Crystal-GNNs,This repository contains a collection of resources and papers on GNN Models on Crystal Solid State Materials.,7,True,,kdmsit/Awesome-Crystal-GNNs,https://github.com/kdmsit/Awesome-Crystal-GNNs,2022-11-15 11:12:18,2024-06-16 16:02:41.000,2024-06-16 16:02:37,34.0,2.0,7.0,4.0,,,,54.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +267,cG-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/cG-SchNet,cG-SchNet - a conditional generative neural network for 3d molecular structures.,7,False,,atomistic-machine-learning/cG-SchNet,https://github.com/atomistic-machine-learning/cG-SchNet,2021-12-02 15:35:18,2023-03-24 12:09:56.000,2023-03-24 12:09:56,28.0,,14.0,3.0,,,3.0,51.0,2022-02-21 13:36:41.000,1.0,1.0,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +268,uncertainty_benchmarking,,general-tool,,https://github.com/ulissigroup/uncertainty_benchmarking,Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions.,7,False,"['benchmarking', 'probabilistic']",ulissigroup/uncertainty_benchmarking,https://github.com/ulissigroup/uncertainty_benchmarking,2019-08-28 19:39:28,2021-06-07 23:29:39.000,2021-06-07 23:27:19,265.0,,7.0,6.0,1.0,,,38.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +269,AdsorbML,,rep-learn,MIT,https://github.com/Open-Catalyst-Project/AdsorbML,,7,False,"['surface-science', 'single-paper']",Open-Catalyst-Project/AdsorbML,https://github.com/Open-Catalyst-Project/AdsorbML,2022-11-30 01:38:20,2024-05-07 21:54:19.000,2023-07-31 16:28:09,56.0,,4.0,7.0,11.0,3.0,1.0,35.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +270,torchchem,,general-tool,MIT,https://github.com/deepchem/torchchem,An experimental repo for experimenting with PyTorch models.,7,False,,deepchem/torchchem,https://github.com/deepchem/torchchem,2020-03-07 17:06:44,2023-03-24 23:13:19.000,2020-05-01 20:12:23,49.0,,13.0,8.0,27.0,5.0,1.0,34.0,,,,5.0,,,1.0,1.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +271,chemlift,,language-models,MIT,https://github.com/lamalab-org/chemlift,Language-interfaced fine-tuning for chemistry.,7,True,,lamalab-org/chemlift,https://github.com/lamalab-org/chemlift,2023-07-10 06:54:07,2023-11-30 10:47:50.000,2023-10-14 16:50:14,36.0,,3.0,1.0,1.0,11.0,7.0,31.0,2023-11-30 19:42:07.000,0.0.1,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +272,ChargE3Net,,ml-dft,MIT,https://github.com/AIforGreatGood/charge3net,Higher-order equivariant neural networks for charge density prediction in materials.,7,True,['rep-learn'],AIforGreatGood/charge3net,https://github.com/AIforGreatGood/charge3net,2023-12-16 13:54:56,2024-08-15 14:35:44.000,2024-08-15 14:35:27,12.0,8.0,6.0,5.0,1.0,,3.0,28.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +273,Mat2Spec,,ml-dft,MIT,https://github.com/gomes-lab/Mat2Spec,Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings.,7,False,['spectroscopy'],gomes-lab/Mat2Spec,https://github.com/gomes-lab/Mat2Spec,2022-01-17 11:45:57,2022-04-17 17:12:29.000,2022-04-17 17:12:29,8.0,,10.0,,,,,27.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +274,escnn_jax,,rep-learn,https://github.com/emilemathieu/escnn_jax/blob/master/LICENSE,https://github.com/emilemathieu/escnn_jax,Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/.,7,False,,emilemathieu/escnn_jax,https://github.com/emilemathieu/escnn_jax,2023-06-15 09:45:45,2023-06-28 14:40:32.000,2023-06-28 14:39:56,203.0,,2.0,,,,,26.0,,,,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +275,LLM-Prop,,language-models,MIT,https://github.com/vertaix/LLM-Prop,A repository for the LLM-Prop implementation.,7,True,,vertaix/LLM-Prop,https://github.com/vertaix/LLM-Prop,2022-10-16 19:15:21,2024-04-26 14:20:54.000,2024-04-26 14:20:54,175.0,,5.0,2.0,,1.0,1.0,25.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +276,Libnxc,,ml-dft,MPL-2.0,https://github.com/semodi/libnxc,A library for using machine-learned exchange-correlation functionals for density-functional theory.,7,False,"['lang-cpp', 'lang-fortran']",semodi/libnxc,https://github.com/semodi/libnxc,2020-07-01 18:21:50,2021-09-18 14:53:52.000,2021-08-14 16:26:32,100.0,,4.0,2.0,3.0,13.0,3.0,16.0,,,2.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +277,COSMO Software Cookbook,,educational,BSD-3-Clause,https://github.com/lab-cosmo/atomistic-cookbook,The COSMO cookbook contains recipes for atomic-scale modelling for materials and molecules.,7,True,,lab-cosmo/software-cookbook,https://github.com/lab-cosmo/atomistic-cookbook,2023-05-23 10:33:47,2024-08-19 08:07:41.000,2024-08-14 17:43:05,69.0,5.0,1.0,16.0,57.0,2.0,10.0,12.0,,,,9.0,,,,,,,,,,,,,3.0,,,,,,,lab-cosmo/atomistic-cookbook,,,,,,,,,,,,, +278,NICE,,rep-eng,MIT,https://github.com/lab-cosmo/nice,NICE (N-body Iteratively Contracted Equivariants) is a set of tools designed for the calculation of invariant and..,7,True,,lab-cosmo/nice,https://github.com/lab-cosmo/nice,2020-07-03 08:47:41,2024-04-15 14:39:34.000,2024-04-15 14:39:33,233.0,,3.0,17.0,7.0,2.0,1.0,12.0,,,1.0,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +279,rxngenerator,,generative,MIT,https://github.com/tsudalab/rxngenerator,A generative model for molecular generation via multi-step chemical reactions.,7,False,,tsudalab/rxngenerator,https://github.com/tsudalab/rxngenerator,2021-06-18 07:44:53,2024-07-24 05:27:21.000,2022-08-09 07:21:05,16.0,,3.0,9.0,2.0,1.0,,11.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +280,AIS Square,,datasets,LGPL-3.0,https://github.com/deepmodeling/AIS-Square,"A collaborative and open-source platform for sharing AI for Science datasets, models, and workflows. Home of the..",7,True,"['community', 'model-repository']",deepmodeling/AIS-Square,https://github.com/deepmodeling/AIS-Square,2022-09-13 09:52:30,2024-08-16 07:47:57.000,2023-12-06 03:06:55,469.0,,8.0,8.0,210.0,5.0,1.0,10.0,,,,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +281,rho_learn,,ml-dft,MIT,https://github.com/jwa7/rho_learn,A proof-of-concept framework for torch-based learning of the electron density and related scalar fields.,7,False,,jwa7/rho_learn,https://github.com/jwa7/rho_learn,2023-03-27 16:59:34,2024-08-16 10:00:52.000,2024-03-20 15:20:39,111.0,,1.0,,4.0,,,3.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +282,MAChINE,,educational,MIT,https://github.com/aimat-lab/MAChINE,Client-Server Web App to introduce usage of ML in materials science to beginners.,7,False,,aimat-lab/MAChINE,https://github.com/aimat-lab/MAChINE,2023-04-17 14:29:06,2023-09-29 14:20:12.000,2023-09-29 10:20:31,1026.0,,,,7.0,9.0,23.0,1.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +283,ML4pXRDs,,rep-learn,MIT,https://github.com/aimat-lab/ML4pXRDs,Contains code to train neural networks based on simulated powder XRDs from synthetic crystals.,7,False,"['xrd', 'single-paper']",aimat-lab/ML4pXRDs,https://github.com/aimat-lab/ML4pXRDs,2022-12-01 16:24:29,2023-07-14 08:17:06.000,2023-07-14 08:17:04,1320.0,,,3.0,,,,,2023-03-22 11:04:31.000,1.0,1.0,,,,,,,,,,0.0,,,,3.0,,,,,,2.0,,,,,,,,,,,,,, +284,The Collection of Database and Dataset Resources in Materials Science,,community,,https://github.com/sedaoturak/data-resources-for-materials-science,"A list of databases, datasets and books/handbooks where you can find materials properties for machine learning..",6,True,['datasets'],sedaoturak/data-resources-for-materials-science,https://github.com/sedaoturak/data-resources-for-materials-science,2021-02-20 06:38:45,2024-06-07 15:51:11.000,2024-06-07 15:51:11,30.0,1.0,40.0,12.0,2.0,1.0,1.0,249.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +285,DeepH-E3,,ml-dft,MIT,https://github.com/Xiaoxun-Gong/DeepH-E3,General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian.,6,False,['magnetism'],Xiaoxun-Gong/DeepH-E3,https://github.com/Xiaoxun-Gong/DeepH-E3,2023-03-16 11:25:58,2023-04-04 13:27:01.000,2023-04-04 13:26:27,16.0,,16.0,6.0,,9.0,6.0,61.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +286,Applied AI for Materials,,educational,,https://github.com/WardLT/applied-ai-for-materials,Course materials for Applied AI for Materials Science and Engineering.,6,False,,WardLT/applied-ai-for-materials,https://github.com/WardLT/applied-ai-for-materials,2020-10-12 19:39:06,2022-03-12 02:26:58.000,2022-03-12 02:26:41,107.0,,31.0,4.0,13.0,5.0,,58.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +287,DeepDFT,,ml-dft,MIT,https://github.com/peterbjorgensen/DeepDFT,Official implementation of DeepDFT model.,6,False,,peterbjorgensen/DeepDFT,https://github.com/peterbjorgensen/DeepDFT,2020-11-03 11:51:15,2023-02-28 15:37:49.000,2023-02-28 15:37:37,128.0,,7.0,1.0,,,5.0,54.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +288,ANI-1x Datasets,,datasets,MIT,https://github.com/aiqm/ANI1x_datasets,"The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for organic molecules.",6,False,,aiqm/ANI1x_datasets,https://github.com/aiqm/ANI1x_datasets,2019-09-17 18:19:28,2022-04-11 17:25:55.000,2022-04-11 17:25:55,12.0,,5.0,5.0,,4.0,3.0,53.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +289,COMP6 Benchmark dataset,,datasets,MIT,https://github.com/isayev/COMP6,COMP6 Benchmark dataset for ML potentials.,6,False,,isayev/COMP6,https://github.com/isayev/COMP6,2017-12-29 16:58:35,2018-07-09 23:56:35.000,2018-07-09 23:56:34,27.0,,4.0,5.0,,2.0,1.0,39.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +290,MACE-Layer,,rep-learn,MIT,https://github.com/ACEsuit/mace-layer,Higher order equivariant graph neural networks for 3D point clouds.,6,False,,ACEsuit/mace-layer,https://github.com/ACEsuit/mace-layer,2022-11-09 17:03:41,2023-06-27 15:32:49.000,2023-06-06 10:09:58,19.0,,6.0,5.0,2.0,1.0,,33.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +291,MACE-tutorials,,educational,MIT,https://github.com/ilyes319/mace-tutorials,Another set of tutorials for the MACE interatomic potential by one of the authors.,6,True,"['ml-iap', 'rep-learn', 'md']",ilyes319/mace-tutorials,https://github.com/ilyes319/mace-tutorials,2023-09-11 18:09:18,2024-07-16 12:45:45.000,2024-07-16 12:45:42,7.0,2.0,9.0,3.0,,1.0,,31.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +292,charge_transfer_nnp,,rep-learn,MIT,https://github.com/pfnet-research/charge_transfer_nnp,Graph neural network potential with charge transfer.,6,False,['electrostatics'],pfnet-research/charge_transfer_nnp,https://github.com/pfnet-research/charge_transfer_nnp,2022-04-06 01:48:18,2022-04-06 01:53:35.000,2022-04-06 01:53:22,1.0,,8.0,12.0,,,,28.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +293,MLIP-3,,ml-iap,BSD-2-Clause,https://gitlab.com/ashapeev/mlip-3,MLIP-3: Active learning on atomic environments with Moment Tensor Potentials (MTP).,6,False,['lang-cpp'],,,2023-04-24 14:05:53,2023-04-24 14:05:53.000,,,,6.0,,,23.0,6.0,25.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,ashapeev/mlip-3,https://gitlab.com/ashapeev/mlip-3,, +294,GLAMOUR,,rep-learn,MIT,https://github.com/learningmatter-mit/GLAMOUR,Graph Learning over Macromolecule Representations.,6,False,['single-paper'],learningmatter-mit/GLAMOUR,https://github.com/learningmatter-mit/GLAMOUR,2021-08-20 18:16:40,2022-12-31 17:56:21.000,2022-12-31 17:56:21,14.0,,6.0,3.0,,,8.0,20.0,2021-08-23 18:58:52.000,0.1,1.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +295,SA-GPR,,rep-eng,LGPL-3.0,https://github.com/dilkins/TENSOAP,Public repository for symmetry-adapted Gaussian Process Regression (SA-GPR).,6,True,['lang-c'],dilkins/TENSOAP,https://github.com/dilkins/TENSOAP,2020-05-04 14:19:01,2024-07-23 13:03:45.000,2024-07-23 13:03:44,26.0,1.0,13.0,3.0,10.0,2.0,5.0,19.0,2020-12-17 16:51:47.000,2020.0,1.0,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +296,EquivariantOperators.jl,,math,MIT,https://github.com/aced-differentiate/EquivariantOperators.jl,This package is deprecated. Functionalities are migrating to Porcupine.jl.,6,True,['lang-julia'],aced-differentiate/EquivariantOperators.jl,https://github.com/aced-differentiate/EquivariantOperators.jl,2021-11-29 03:36:21,2023-09-27 18:34:44.000,2023-09-27 18:34:44,62.0,,,4.0,,,,18.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +297,CatGym,,reinforcement-learning,GPL,https://github.com/ulissigroup/catgym,Surface segregation using Deep Reinforcement Learning.,6,False,,ulissigroup/catgym,https://github.com/ulissigroup/catgym,2019-08-06 19:25:27,2021-08-30 17:05:36.000,2021-08-30 17:05:32,162.0,,2.0,4.0,,2.0,,11.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +298,testing-framework,,ml-iap,,https://github.com/libAtoms/testing-framework,The purpose of this repository is to aid the testing of a large number of interatomic potentials for a variety of..,6,False,['benchmarking'],libAtoms/testing-framework,https://github.com/libAtoms/testing-framework,2020-03-04 11:43:15,2022-02-10 17:23:46.000,2022-02-10 17:23:46,225.0,,6.0,16.0,10.0,5.0,3.0,11.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +299,PANNA,,ml-iap,MIT,https://gitlab.com/PANNAdevs/panna,A package to train and validate all-to-all connected network models for BP[1] and modified-BP[2] type local atomic..,6,False,['benchmarking'],,,2018-11-09 10:47:48,2018-11-09 10:47:48.000,,,,10.0,,,,,9.0,,,2.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,PANNAdevs/panna,https://gitlab.com/PANNAdevs/panna,, +300,ML for catalysis tutorials,,educational,MIT,https://github.com/ulissigroup/ml_catalysis_tutorials,A jupyter book repo for tutorial on how to use OCP ML models for catalysis.,6,False,,ulissigroup/ml_catalysis_tutorials,https://github.com/ulissigroup/ml_catalysis_tutorials,2022-10-28 20:37:30,2022-10-31 18:06:07.000,2022-10-31 17:49:25,40.0,,1.0,4.0,,,,8.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +301,COSMO Toolbox,,math,,https://github.com/lab-cosmo/toolbox,Assorted libraries and utilities for atomistic simulation analysis.,6,True,['lang-cpp'],lab-cosmo/toolbox,https://github.com/lab-cosmo/toolbox,2014-05-20 11:23:13,2024-03-19 13:27:28.000,2024-03-19 13:27:02,107.0,,5.0,27.0,1.0,,,7.0,,,,9.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +302,fplib,,rep-eng,MIT,https://github.com/zhuligs/fplib,a fingerprint library.,6,False,"['lang-c', 'single-paper']",zhuligs/fplib,https://github.com/zhuligs/fplib,2015-09-07 08:18:27,2022-02-09 05:31:21.000,2022-02-09 05:31:12,37.0,,2.0,3.0,,,3.0,7.0,2021-02-03 21:40:23.000,pub,1.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +303,SOAPxx,,rep-eng,GPL-2.0,https://github.com/capoe/soapxx,A SOAP implementation.,6,False,['lang-cpp'],capoe/soapxx,https://github.com/capoe/soapxx,2016-03-29 10:00:00,2020-03-27 13:47:44.000,2020-03-27 13:47:36,289.0,,3.0,3.0,1.0,,2.0,7.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +304,Equisolve,,general-tool,BSD-3-Clause,https://github.com/lab-cosmo/equisolve,A ML toolkit package utilizing the metatensor data format to build models for the prediction of equivariant properties..,6,True,['ml-iap'],lab-cosmo/equisolve,https://github.com/lab-cosmo/equisolve,2022-10-04 15:29:19,2023-10-27 10:03:59.000,2023-10-27 09:55:17,55.0,,1.0,17.0,43.0,19.0,4.0,5.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +305,MEGAN: Multi Explanation Graph Attention Student,,xai,MIT,https://github.com/aimat-lab/graph_attention_student,Minimal implementation of graph attention student model architecture.,6,True,['rep-learn'],aimat-lab/graph_attention_student,https://github.com/aimat-lab/graph_attention_student,2022-07-28 06:22:50,2024-08-19 08:43:44.000,2024-08-19 08:40:19,92.0,13.0,1.0,3.0,1.0,,2.0,5.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +306,Cephalo,,language-models,Apache-2.0,https://github.com/lamm-mit/Cephalo,Multimodal Vision-Language Models for Bio-Inspired Materials Analysis and Design.,6,True,"['generative', 'multimodal', 'pretrained']",lamm-mit/Cephalo,https://github.com/lamm-mit/Cephalo,2024-05-28 12:29:13,2024-07-23 09:27:58.000,2024-07-23 09:27:57,24.0,24.0,1.0,1.0,,,,5.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +307,cnine,,math,,https://github.com/risi-kondor/cnine,Cnine tensor library.,6,False,['lang-cpp'],risi-kondor/cnine,https://github.com/risi-kondor/cnine,2022-10-07 20:54:54,2024-08-19 04:16:23.000,2024-08-09 03:21:10,381.0,10.0,4.0,2.0,7.0,,1.0,4.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,https://risi-kondor.github.io/cnine/, +308,soap_turbo,,rep-eng,https://github.com/libAtoms/soap_turbo/blob/master/LICENSE.md,https://github.com/libAtoms/soap_turbo,soap_turbo comprises a series of libraries to be used in combination with QUIP/GAP and TurboGAP.,6,False,['lang-fortran'],libAtoms/soap_turbo,https://github.com/libAtoms/soap_turbo,2021-03-19 15:20:25,2024-06-28 11:53:50.000,2023-05-24 09:42:00,36.0,,8.0,8.0,,5.0,3.0,4.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +309,KSR-DFT,,ml-dft,Apache-2.0,https://github.com/pedersor/ksr_dft,Kohn-Sham regularizer for machine-learned DFT functionals.,6,False,,pedersor/ksr_dft,https://github.com/pedersor/ksr_dft,2023-03-01 17:24:48,2023-03-04 07:20:22.000,2023-03-04 07:20:18,466.0,,,1.0,,,,4.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +310,pyLODE,,rep-eng,Apache-2.0,https://github.com/ceriottm/lode,Pythonic implementation of LOng Distance Equivariants.,6,False,['electrostatics'],ceriottm/lode,https://github.com/ceriottm/lode,2022-01-19 17:01:38,2023-07-05 09:57:29.000,2023-07-05 09:57:14,241.0,,1.0,2.0,,1.0,,3.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +311,ACEpsi.jl,,ml-wft,MIT,https://github.com/ACEsuit/ACEpsi.jl,ACE wave function parameterizations.,6,False,"['rep-eng', 'lang-julia']",ACEsuit/ACEpsi.jl,https://github.com/ACEsuit/ACEpsi.jl,2022-10-21 03:51:18,2024-04-12 06:18:19.000,2023-10-05 21:21:35,162.0,,,4.0,16.0,5.0,4.0,2.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +312,OPTIMADE providers dashboard,,datasets,,https://www.optimade.org/providers-dashboard/,A dashboard of known providers.,6,False,,Materials-Consortia/providers-dashboard,https://github.com/Materials-Consortia/providers-dashboard,2020-06-17 16:15:07,2024-08-19 06:31:46.000,2024-08-01 23:27:42,139.0,21.0,3.0,19.0,141.0,10.0,18.0,1.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +313,Computational Autonomy for Materials Discovery (CAMD),,materials-discovery,Apache-2.0,https://github.com/ulissigroup/CAMD,Agent-based sequential learning software for materials discovery.,6,False,,ulissigroup/CAMD,https://github.com/ulissigroup/CAMD,2023-01-10 19:42:57,2023-01-10 19:49:35.000,2023-01-10 19:49:13,1336.0,,,1.0,,,,1.0,,,,17.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,, +314,ACE1Pack.jl,,ml-iap,MIT,https://github.com/ACEsuit/ACE1pack.jl,"Provides convenience functionality for the usage of ACE1.jl, ACEfit.jl, JuLIP.jl for fitting interatomic potentials..",6,True,['lang-julia'],ACEsuit/ACE1pack.jl,https://github.com/ACEsuit/ACE1pack.jl,2023-08-21 16:25:00,2023-08-21 16:30:19.000,2023-08-21 15:48:54,547.0,,,1.0,,,,,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,https://acesuit.github.io/ACE1pack.jl, +315,AMP,,rep-eng,,https://bitbucket.org/andrewpeterson/amp/,Amp is an open-source package designed to easily bring machine-learning to atomistic calculations.,6,False,,,,,2023-01-25 17:30:41.112,,,,25.0,,,,,,2023-01-25 17:30:41.112,1.0.1,3.0,,amp-atomistics,,,,https://pypi.org/project/amp-atomistics,2023-01-25 17:30:41.112,,78.0,78.0,,,,3.0,,,,,,,,,,,,,,,,,,,https://amp.readthedocs.io/, +316,COATI,,generative,Apache 2.0,https://github.com/terraytherapeutics/COATI,COATI: multi-modal contrastive pre-training for representing and traversing chemical space.,5,True,"['drug-discovery', 'multimodal', 'pretrained', 'rep-learn']",terraytherapeutics/COATI,https://github.com/terraytherapeutics/COATI,2023-08-11 14:56:39,2024-03-23 18:06:26.000,2024-03-23 18:06:26,16.0,,5.0,2.0,6.0,1.0,2.0,89.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +317,AI4Science101,,educational,,https://github.com/deepmodeling/AI4Science101,AI for Science.,5,False,,deepmodeling/AI4Science101,https://github.com/deepmodeling/AI4Science101,2022-06-19 02:26:48,2024-04-11 02:15:55.000,2022-09-04 02:06:18,139.0,,13.0,9.0,29.0,2.0,1.0,83.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +318,crystal-text-llm,,language-models,CC-BY-NC-4.0,https://github.com/facebookresearch/crystal-text-llm,Large language models to generate stable crystals.,5,True,['materials-discovery'],facebookresearch/crystal-text-llm,https://github.com/facebookresearch/crystal-text-llm,2024-02-05 22:29:12,2024-06-18 17:10:52.000,2024-06-18 17:10:52,13.0,2.0,10.0,5.0,3.0,7.0,2.0,63.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +319,The Perovskite Database Project,,datasets,,https://github.com/Jesperkemist/perovskitedatabase,"Perovskite Database Project aims at making all perovskite device data, both past and future, available in a form..",5,True,['community'],Jesperkemist/perovskitedatabase,https://github.com/Jesperkemist/perovskitedatabase,2021-01-17 14:26:45,2024-03-07 11:09:21.000,2024-03-07 11:09:17,44.0,,18.0,3.0,7.0,1.0,,58.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +320,SchNOrb,,ml-wft,MIT,https://github.com/atomistic-machine-learning/SchNOrb,Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions.,5,False,,atomistic-machine-learning/SchNOrb,https://github.com/atomistic-machine-learning/SchNOrb,2019-09-17 12:41:48,2019-09-17 14:31:47.000,2019-09-17 14:31:19,2.0,,19.0,5.0,,1.0,,58.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +321,Machine Learning for Materials Hard and Soft,,educational,,https://github.com/CompPhysVienna/MLSummerSchoolVienna2022,ESI-DCAFM-TACO-VDSP Summer School on Machine Learning for Materials Hard and Soft.,5,False,,CompPhysVienna/MLSummerSchoolVienna2022,https://github.com/CompPhysVienna/MLSummerSchoolVienna2022,2022-07-01 08:42:41,2022-07-22 08:10:24.000,2022-07-22 08:10:24,49.0,,20.0,1.0,14.0,,,34.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +322,Joint Multidomain Pre-Training (JMP),,uip,CC-BY-NC-4.0,https://github.com/facebookresearch/JMP,Code for From Molecules to Materials Pre-training Large Generalizable Models for Atomic Property Prediction.,5,True,"['pretrained', 'ml-iap', 'general-tool']",facebookresearch/JMP,https://github.com/facebookresearch/JMP,2024-03-14 23:10:10,2024-06-20 04:11:08.000,2024-05-07 08:19:12,1.0,,4.0,4.0,1.0,1.0,,32.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +323,xDeepH,,ml-dft,LGPL-3.0,https://github.com/mzjb/xDeepH,Extended DeepH (xDeepH) method for magnetic materials.,5,False,"['magnetism', 'lang-julia']",mzjb/xDeepH,https://github.com/mzjb/xDeepH,2023-02-23 12:56:49,2023-06-14 11:44:53.000,2023-06-14 11:44:46,4.0,,3.0,3.0,,1.0,,30.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +324,Autobahn,,rep-learn,MIT,https://github.com/risilab/Autobahn,Repository for Autobahn: Automorphism Based Graph Neural Networks.,5,False,,risilab/Autobahn,https://github.com/risilab/Autobahn,2021-03-02 01:14:40,2022-03-01 21:04:09.000,2022-03-01 21:04:04,11.0,,2.0,5.0,,,,30.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +325,milad,,rep-eng,GPL-3.0,https://github.com/muhrin/milad,Moment Invariants Local Atomic Descriptor.,5,False,['generative'],muhrin/milad,https://github.com/muhrin/milad,2020-04-23 09:14:24,2022-12-03 10:40:05.000,2022-12-03 10:39:59,110.0,,1.0,4.0,,,,29.0,,,,1.0,,,2.0,2.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +326,SciBot,,language-models,,https://github.com/CFN-softbio/SciBot,SciBot is a simple demo of building a domain-specific chatbot for science.,5,True,['ai-agent'],CFN-softbio/SciBot,https://github.com/CFN-softbio/SciBot,2023-06-12 12:41:44,2024-04-19 18:34:24.000,2024-04-19 18:17:00,22.0,,8.0,6.0,,,,28.0,,,,1.0,,,1.0,1.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +327,ML-DFT,,ml-dft,MIT,https://github.com/MihailBogojeski/ml-dft,A package for density functional approximation using machine learning.,5,False,,MihailBogojeski/ml-dft,https://github.com/MihailBogojeski/ml-dft,2020-09-14 22:15:56,2020-09-18 16:36:30.000,2020-09-18 16:36:30,9.0,,7.0,2.0,,1.0,1.0,23.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +328,Coarse-Graining-Auto-encoders,,unsupervised,,https://github.com/learningmatter-mit/Coarse-Graining-Auto-encoders,Implementation of coarse-graining Autoencoders.,5,False,['single-paper'],learningmatter-mit/Coarse-Graining-Auto-encoders,https://github.com/learningmatter-mit/Coarse-Graining-Auto-encoders,2019-09-16 15:27:57,2019-08-16 21:39:34.000,2019-08-16 21:39:33,14.0,,7.0,6.0,,,,21.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +329,CSPML (crystal structure prediction with machine learning-based element substitution),,materials-discovery,,https://github.com/Minoru938/CSPML,Original implementation of CSPML.,5,True,['structure-prediction'],minoru938/cspml,https://github.com/Minoru938/CSPML,2022-01-15 10:59:27,2024-07-09 12:40:53.000,2024-07-09 12:40:53,23.0,16.0,8.0,2.0,,2.0,1.0,18.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +330,NequIP-JAX,,ml-iap,,https://github.com/mariogeiger/nequip-jax,JAX implementation of the NequIP interatomic potential.,5,True,,mariogeiger/nequip-jax,https://github.com/mariogeiger/nequip-jax,2023-03-08 04:18:28,2023-11-01 20:35:48.000,2023-11-01 20:35:44,39.0,,3.0,1.0,2.0,1.0,,17.0,2023-06-22 22:36:36.000,1.1.0,3.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +331,FieldSchNet,,rep-learn,MIT,https://github.com/atomistic-machine-learning/field_schnet,Deep neural network for molecules in external fields.,5,False,,atomistic-machine-learning/field_schnet,https://github.com/atomistic-machine-learning/field_schnet,2020-11-18 10:26:59,2022-05-19 09:28:38.000,2022-05-19 09:28:38,26.0,,4.0,2.0,1.0,1.0,,16.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +332,Does this material exist?,,community,MIT,https://thismaterialdoesnotexist.com/,Vote on whether you think predicted crystal structures could be synthesised.,5,True,"['for-fun', 'materials-discovery']",ml-evs/this-material-does-not-exist,https://github.com/ml-evs/this-material-does-not-exist,2023-12-01 18:16:28,2024-07-29 09:50:18.000,2024-04-10 12:32:06,16.0,,3.0,2.0,2.0,2.0,,15.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +333,3DSC Database,,datasets,https://github.com/aimat-lab/3DSC/blob/main/LICENSE.md,https://github.com/aimat-lab/3DSC,Repo for the paper publishing the superconductor database with 3D crystal structures.,5,True,"['superconductors', 'materials-discovery']",aimat-lab/3DSC,https://github.com/aimat-lab/3DSC,2021-11-02 09:07:57,2024-01-08 09:21:11.000,2024-01-08 09:21:11,53.0,,4.0,2.0,,1.0,,15.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +334,SCFNN,,rep-learn,MIT,https://github.com/andy90/SCFNN,Self-consistent determination of long-range electrostatics in neural network potentials.,5,False,"['lang-cpp', 'electrostatics', 'single-paper']",andy90/SCFNN,https://github.com/andy90/SCFNN,2021-09-22 12:02:00,2022-01-30 02:29:03.000,2022-01-24 09:40:40,10.0,,8.0,2.0,,,,15.0,2022-01-30 02:29:04.000,1.0.0,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +335,CraTENet,,rep-learn,MIT,https://github.com/lantunes/CraTENet,An attention-based deep neural network for thermoelectric transport properties.,5,False,['transport-phenomena'],lantunes/CraTENet,https://github.com/lantunes/CraTENet,2022-06-30 10:40:06,2023-04-05 01:13:22.000,2023-04-05 01:13:11,24.0,,1.0,1.0,,,,13.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +336,ACEHAL,,active-learning,,https://github.com/ACEsuit/ACEHAL,Hyperactive Learning (HAL) Python interface for building Atomic Cluster Expansion potentials.,5,True,['lang-julia'],ACEsuit/ACEHAL,https://github.com/ACEsuit/ACEHAL,2023-02-24 17:33:47,2023-10-01 12:19:41.000,2023-09-21 21:50:43,121.0,,7.0,5.0,15.0,4.0,6.0,11.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +337,BERT-PSIE-TC,,language-models,MIT,https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC,A dataset of Curie temperatures automatically extracted from scientific literature with the use of the BERT-PSIE..,5,True,['magnetism'],StefanoSanvitoGroup/BERT-PSIE-TC,https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC,2023-01-25 10:27:26,2023-08-18 11:47:45.000,2023-08-18 12:48:31,36.0,,3.0,1.0,,,,11.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +338,SOMD,,md,AGPL-3.0,https://github.com/initqp/somd,Molecular dynamics package designed for the SIESTA DFT code.,5,True,"['ml-iap', 'active-learning']",initqp/somd,https://github.com/initqp/somd,2023-03-09 19:00:41,2024-08-17 16:10:44.000,2024-08-17 16:07:39,303.0,4.0,2.0,1.0,11.0,,1.0,11.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +339,QMLearn,,ml-esm,MIT,http://qmlearn.rutgers.edu/,Quantum Machine Learning by learning one-body reduced density matrices in the AO basis...,5,False,,,,2022-02-15 13:42:13,2022-02-15 13:42:13.000,,,,3.0,,,,,11.0,,,0.0,,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,pavanello-research-group/qmlearn,https://gitlab.com/pavanello-research-group/qmlearn,, +340,SciGlass,,datasets,MIT,https://github.com/drcassar/SciGlass,The database contains a vast set of data on the properties of glass materials.,5,True,,drcassar/SciGlass,https://github.com/drcassar/SciGlass,2019-06-19 19:36:32,2023-08-27 13:46:44.000,2023-08-27 13:46:44,28.0,,3.0,1.0,,,,10.0,2023-08-27 13:48:09.000,2.0.1,1.0,2.0,,,,,,,,,1.0,,,,3.0,,,,,,16.0,,,,,,,,,,,,,, +341,InfGCN for Electron Density Estimation,,ml-dft,MIT,https://github.com/ccr-cheng/InfGCN-pytorch,Official implementation of the NeurIPS 23 spotlight paper of InfGCN.,5,True,['rep-learn'],ccr-cheng/infgcn-pytorch,https://github.com/ccr-cheng/InfGCN-pytorch,2023-10-01 21:21:40,2023-12-05 01:31:19.000,2023-12-05 01:31:14,3.0,,3.0,1.0,,,3.0,10.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +342,GN-MM,,ml-iap,MIT,https://gitlab.com/zaverkin_v/gmnn,The Gaussian Moment Neural Network (GM-NN) package developed for large-scale atomistic simulations employing atomistic..,5,False,"['active-learning', 'md', 'rep-eng', 'magnetism']",,,2021-09-19 15:56:31,2021-09-19 15:56:31.000,,,,4.0,,,,,10.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,zaverkin_v/gmnn,https://gitlab.com/zaverkin_v/gmnn,, +343,MAPI_LLM,,language-models,MIT,https://github.com/maykcaldas/MAPI_LLM,A LLM application developed during the LLM March MADNESS Hackathon https://doi.org/10.1039/D3DD00113J.,5,True,"['ai-agent', 'dataset']",maykcaldas/MAPI_LLM,https://github.com/maykcaldas/MAPI_LLM,2023-03-30 04:24:54,2024-04-20 03:16:17.000,2024-04-11 22:22:28,31.0,,2.0,1.0,7.0,,,8.0,2023-06-29 18:48:44.000,0.0.1,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +344,EGraFFBench,,rep-learn,,https://github.com/M3RG-IITD/MDBENCHGNN,,5,False,"['single-paper', 'benchmarking', 'ml-iap']",M3RG-IITD/MDBENCHGNN,https://github.com/M3RG-IITD/MDBENCHGNN,2023-07-06 18:15:34,2023-11-19 05:16:12.000,2023-11-19 05:14:44,161.0,,,,,4.0,,8.0,2023-07-16 05:46:38.000,0.1.0,1.0,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +345,GDB-9-Ex9 and ORNL_AISD-Ex,,datasets,,https://github.com/ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python,Distributed computing workflow for generation and analysis of large scale molecular datasets obtained running multi-..,5,True,,ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python,https://github.com/ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python,2023-01-06 18:09:54,2023-08-11 16:49:35.000,2023-08-11 16:49:35,47.0,,5.0,6.0,13.0,2.0,,6.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +346,MolSLEPA,,generative,MIT,https://github.com/tsudalab/MolSLEPA,Interpretable Fragment-based Molecule Design with Self-learning Entropic Population Annealing.,5,False,['xai'],tsudalab/MolSLEPA,https://github.com/tsudalab/MolSLEPA,2023-04-10 15:04:55,2023-04-13 12:48:49.000,2023-04-13 12:48:49,11.0,,1.0,8.0,2.0,,,5.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +347,MXenes4HER,,rep-eng,GPL-3.0,https://github.com/cnislab/MXenes4HER,Predicting hydrogen evolution (HER) activity over 4500 MXene materials https://doi.org/10.1039/D3TA00344B.,5,False,"['materials-discovery', 'catalysis', 'scikit-learn', 'single-paper']",cnislab/MXenes4HER,https://github.com/cnislab/MXenes4HER,2022-11-28 09:27:36,2023-02-27 18:08:05.000,2023-02-27 18:08:05,67.0,,3.0,1.0,1.0,,,5.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +348,COSMO tools,,others,,https://github.com/lab-cosmo/cosmo-tools,"Scripts, jupyter nbs, and general helpful stuff from COSMO by COSMO.",5,False,,lab-cosmo/cosmo-tools,https://github.com/lab-cosmo/cosmo-tools,2018-11-06 09:40:00,2024-05-24 05:53:06.000,2024-05-24 05:53:06,63.0,1.0,4.0,23.0,,,,4.0,,,,4.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,True +349,q-pac,,ml-esm,MIT,https://gitlab.com/jmargraf/qpac,Kernel charge equilibration method.,5,False,['electrostatics'],,,2020-11-15 20:11:27,2020-11-15 20:11:27.000,,,,4.0,,,2.0,,4.0,,,0.0,,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,jmargraf/qpac,https://gitlab.com/jmargraf/qpac,, +350,paper-ml-robustness-material-property,,unsupervised,BSD-3-Clause,https://github.com/mathsphy/paper-ml-robustness-material-property,A critical examination of robustness and generalizability of machine learning prediction of materials properties.,5,False,"['datasets', 'single-paper']",mathsphy/paper-ml-robustness-material-property,https://github.com/mathsphy/paper-ml-robustness-material-property,2023-02-21 02:38:13,2023-04-13 01:18:02.000,2023-04-13 01:18:02,3.0,,3.0,1.0,,,,4.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +351,SISSO++,,rep-eng,Apache-2.0,https://gitlab.com/sissopp_developers/sissopp,C++ Implementation of SISSO with python bindings.,5,False,['lang-cpp'],,,2021-04-30 14:20:59,2021-04-30 14:20:59.000,,,,3.0,,,3.0,18.0,3.0,,,1.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,sissopp_developers/sissopp,https://gitlab.com/sissopp_developers/sissopp,, +352,Alchemical learning,,ml-iap,BSD-3-Clause,https://github.com/Luthaf/alchemical-learning,Code for the Modeling high-entropy transition metal alloys with alchemical compression article.,5,False,,Luthaf/alchemical-learning,https://github.com/Luthaf/alchemical-learning,2021-12-02 17:02:00,2023-04-24 18:35:45.000,2023-04-07 10:19:10,120.0,,1.0,7.0,1.0,,4.0,2.0,,,,10.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +353,Visual Graph Datasets,,datasets,MIT,https://github.com/aimat-lab/visual_graph_datasets,Datasets for the training of graph neural networks (GNNs) and subsequent visualization of attributional explanations..,5,False,"['xai', 'rep-learn']",aimat-lab/visual_graph_datasets,https://github.com/aimat-lab/visual_graph_datasets,2023-06-01 11:33:18,2024-06-26 14:06:17.000,2024-06-26 14:06:13,53.0,3.0,2.0,3.0,,1.0,,1.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +354,linear-regression-benchmarks,,datasets,MIT,https://github.com/BingqingCheng/linear-regression-benchmarks,Data sets used for linear regression benchmarks.,5,False,"['benchmarking', 'single-paper']",BingqingCheng/linear-regression-benchmarks,https://github.com/BingqingCheng/linear-regression-benchmarks,2020-04-16 20:48:28,2022-01-26 08:29:46.000,2022-01-26 08:29:46,24.0,,,3.0,2.0,,,1.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +355,"Data Handling, DoE and Statistical Analysis for Material Chemists",,educational,GPL-3.0,https://github.com/Teoroo-CMC/DoE_Course_Material,"Notebooks for workshops of DoE course, hosted by the Computational Materials Chemistry group at Uppsala University.",5,False,,Teoroo-CMC/DoE_Course_Material,https://github.com/Teoroo-CMC/DoE_Course_Material,2023-05-22 08:11:41,2023-06-26 12:48:17.000,2023-06-26 12:48:15,157.0,,15.0,2.0,1.0,,,1.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +356,Per-Site CGCNN,,rep-learn,MIT,https://github.com/learningmatter-mit/per-site_cgcnn,Crystal graph convolutional neural networks for predicting material properties.,5,False,"['pretrained', 'single-paper']",learningmatter-mit/per-site_cgcnn,https://github.com/learningmatter-mit/per-site_cgcnn,2023-05-30 18:59:03,2023-06-05 17:38:46.000,2023-06-05 17:38:41,28.0,,,,,,,1.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +357,Per-site PAiNN,,rep-learn,MIT,https://github.com/learningmatter-mit/per-site_painn,Fork of PaiNN for PerovskiteOrderingGCNNs.,5,False,"['probabilistic', 'pretrained', 'single-paper']",learningmatter-mit/per-site_painn,https://github.com/learningmatter-mit/per-site_painn,2023-06-04 14:23:49,2023-06-05 17:35:19.000,2023-06-05 17:30:34,123.0,,1.0,,,,,1.0,,,,10.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +358,Geometric-GNNs,,community,,https://github.com/AlexDuvalinho/geometric-gnns,List of Geometric GNNs for 3D atomic systems.,4,False,"['datasets', 'educational', 'rep-learn']",AlexDuvalinho/geometric-gnns,https://github.com/AlexDuvalinho/geometric-gnns,2023-08-31 09:10:32,2024-02-29 16:25:54.000,2024-02-29 16:25:53,37.0,,6.0,1.0,3.0,,1.0,87.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +359,ML-in-chemistry-101,,educational,,https://github.com/BingqingCheng/ML-in-chemistry-101,The course materials for Machine Learning in Chemistry 101.,4,False,,BingqingCheng/ML-in-chemistry-101,https://github.com/BingqingCheng/ML-in-chemistry-101,2020-02-09 17:47:07,2020-10-19 08:10:31.000,2020-10-19 08:10:30,13.0,,17.0,2.0,,,,67.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +360,MAGUS,,materials-discovery,,https://gitlab.com/bigd4/magus,Machine learning And Graph theory assisted Universal structure Searcher.,4,False,"['structure-prediction', 'active-learning']",,,2023-01-31 09:00:23,2023-01-31 09:00:23.000,,,,15.0,,,,,56.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,bigd4/magus,https://gitlab.com/bigd4/magus,, +361,Allegro-Legato,,ml-iap,MIT,https://github.com/ibayashi-hikaru/allegro-legato,An extension of Allegro with enhanced robustness and time-to-failure.,4,False,['md'],ibayashi-hikaru/allegro-legato,https://github.com/ibayashi-hikaru/allegro-legato,2023-01-17 19:46:10,2023-08-03 22:25:11.000,2023-08-03 22:24:35,82.0,,1.0,1.0,,,,19.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +362,glp,,ml-iap,MIT,https://github.com/sirmarcel/glp,tools for graph-based machine-learning potentials in jax.,4,False,,sirmarcel/glp,https://github.com/sirmarcel/glp,2023-03-27 15:19:40,2024-04-09 12:06:56.000,2024-03-20 09:00:27,11.0,,1.0,2.0,3.0,,,17.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +363,Graph Transport Network,,rep-learn,https://github.com/gasteigerjo/gtn/blob/main/LICENSE.md,https://github.com/gasteigerjo/gtn,"Graph transport network (GTN), as proposed in Scalable Optimal Transport in High Dimensions for Graph Distances,..",4,False,['transport-phenomena'],gasteigerjo/gtn,https://github.com/gasteigerjo/gtn,2021-07-11 23:36:22,2023-04-26 14:22:00.000,2023-04-26 14:22:00,9.0,,3.0,2.0,,,,16.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +364,ChemDataWriter,,language-models,MIT,https://github.com/ShuHuang/chemdatawriter,ChemDataWriter is a transformer-based library for automatically generating research books in the chemistry area.,4,False,['literature-data'],ShuHuang/chemdatawriter,https://github.com/ShuHuang/chemdatawriter,2023-09-22 10:05:25,2023-10-07 04:23:47.000,2023-10-07 04:07:59,9.0,,1.0,2.0,,1.0,,14.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +365,SPINNER,,materials-discovery,GPL-3.0,https://github.com/MDIL-SNU/SPINNER,SPINNER (Structure Prediction of Inorganic crystals using Neural Network potentials with Evolutionary and Random..,4,False,"['lang-cpp', 'structure-prediction']",MDIL-SNU/SPINNER,https://github.com/MDIL-SNU/SPINNER,2021-07-15 02:10:58,2024-07-20 05:12:50.000,2021-11-25 07:58:15,102.0,,2.0,1.0,,1.0,,12.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +366,charge-density-models,,ml-dft,MIT,https://github.com/ulissigroup/charge-density-models,Tools to build charge density models using [fairchem](https://github.com/FAIR-Chem/fairchem).,4,False,['rep-learn'],ulissigroup/charge-density-models,https://github.com/ulissigroup/charge-density-models,2022-06-22 13:47:53,2023-11-29 15:07:42.000,2023-11-29 15:07:42,96.0,,2.0,2.0,16.0,1.0,2.0,10.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +367,paper-data-redundancy,,datasets,BSD-3-Clause,https://github.com/mathsphy/paper-data-redundancy,Repo for the paper Exploiting redundancy in large materials datasets for efficient machine learning with less data.,4,False,"['small-data', 'single-paper']",mathsphy/paper-data-redundancy,https://github.com/mathsphy/paper-data-redundancy,2023-06-10 15:00:28,2024-03-22 20:24:35.000,2024-03-22 20:24:34,17.0,,,1.0,,,,7.0,2023-10-11 14:09:07.000,1.0,1.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +368,TensorPotential,,ml-iap,https://github.com/ICAMS/TensorPotential/blob/main/LICENSE.md,https://cortner.github.io/ACEweb/software/,"Tensorpotential is a TensorFlow based tool for development, fitting ML interatomic potentials from electronic..",4,False,,ICAMS/TensorPotential,https://github.com/ICAMS/TensorPotential,2021-12-08 12:10:04,2023-07-10 16:37:18.000,2023-07-10 16:37:18,18.0,,4.0,2.0,2.0,,,7.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +369,Mapping out phase diagrams with generative classifiers,,generative,MIT,https://github.com/arnoldjulian/Mapping-out-phase-diagrams-with-generative-classifiers,Repository for our ``Mapping out phase diagrams with generative models paper.,4,False,['phase-transition'],arnoldjulian/Mapping-out-phase-diagrams-with-generative-classifiers,https://github.com/arnoldjulian/Mapping-out-phase-diagrams-with-generative-classifiers,2023-06-07 21:43:14,2023-06-27 08:12:29.000,2023-06-27 08:12:29,39.0,,2.0,1.0,,,,7.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +370,chemrev-gpr,,educational,,https://github.com/gabor1/chemrev-gpr,Notebooks accompanying the paper on GPR in materials and molecules in Chemical Reviews 2020.,4,False,,gabor1/chemrev-gpr,https://github.com/gabor1/chemrev-gpr,2020-12-18 23:48:06,2021-05-04 19:21:34.000,2021-05-04 19:21:30,10.0,,6.0,4.0,,,,6.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +371,automl-materials,,rep-eng,MIT,https://github.com/mm-tud/automl-materials,AutoML for Regression Tasks on Small Tabular Data in Materials Design.,4,False,"['automl', 'benchmarking', 'single-paper']",mm-tud/automl-materials,https://github.com/mm-tud/automl-materials,2022-10-07 09:49:18,2022-11-15 15:22:54.000,2022-11-15 15:22:45,6.0,,1.0,2.0,,,,5.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +372,ML-atomate,,materials-discovery,GPL-3.0,https://github.com/takahashi-akira-36m/ml_atomate,Machine learning-assisted Atomate code for autonomous computational materials screening.,4,False,"['active-learning', 'workflows']",takahashi-akira-36m/ml_atomate,https://github.com/takahashi-akira-36m/ml_atomate,2023-09-21 08:45:10,2023-11-17 09:54:23.000,2023-11-17 09:51:02,6.0,,1.0,1.0,,,,4.0,2023-09-29 03:52:46.000,stam_m_2023_fix,2.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +373,gkx: Green-Kubo Method in JAX,,rep-learn,MIT,https://github.com/sirmarcel/gkx,Green-Kubo + JAX + MLPs = Anharmonic Thermal Conductivities Done Fast.,4,False,['transport-phenomena'],sirmarcel/gkx,https://github.com/sirmarcel/gkx,2023-04-30 12:25:16,2024-03-20 09:05:20.000,2024-03-20 09:05:14,3.0,,,1.0,,,,3.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +374,ACEatoms,,general-tool,https://github.com/ACEsuit/ACEatoms.jl/blob/main/ASL.md,https://github.com/ACEsuit/ACEatoms.jl,Generic code for modelling atomic properties using ACE.,4,False,['lang-julia'],ACEsuit/ACEatoms.jl,https://github.com/ACEsuit/ACEatoms.jl,2021-03-23 23:50:03,2023-01-13 21:35:06.000,2023-01-13 21:28:08,134.0,,1.0,3.0,14.0,4.0,3.0,2.0,,,,10.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +375,halex,,ml-esm,,https://github.com/ecignoni/halex,Hamiltonian Learning for Excited States https://doi.org/10.48550/arXiv.2311.00844.,4,False,['excited-states'],ecignoni/halex,https://github.com/ecignoni/halex,2023-09-04 06:54:15,2024-02-08 10:20:53.000,2024-02-08 10:20:49,169.0,,,3.0,,1.0,,2.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +376,AI4ChemMat Hands-On Series,,educational,MPL-2.0,https://github.com/ai4chemmat/ai4chemmat.github.io,Hands-On Series organized by Chemistry and Materials working group at Argonne Nat Lab.,4,False,,ai4chemmat/ai4chemmat.github.io,https://github.com/ai4chemmat/ai4chemmat.github.io,2023-03-24 21:25:21,2024-04-24 16:32:18.000,2024-04-24 16:32:18,40.0,,,2.0,,,,1.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +377,magnetism-prediction,,rep-eng,Apache-2.0,https://github.com/dppant/magnetism-prediction,DFT-aided Machine Learning Search for Magnetism in Fe-based Bimetallic Chalcogenides.,4,False,"['magnetism', 'single-paper']",dppant/magnetism-prediction,https://github.com/dppant/magnetism-prediction,2022-09-13 03:58:10,2023-07-19 13:25:49.000,2023-07-19 13:25:49,46.0,,,3.0,,,,1.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +378,ACE Workflows,,ml-iap,,https://github.com/ACEsuit/ACEworkflows,Workflow Examples for ACE Models.,4,False,"['lang-julia', 'workflows']",ACEsuit/ACEworkflows,https://github.com/ACEsuit/ACEworkflows,2023-04-04 16:57:36,2023-10-12 18:01:00.000,2023-10-12 18:00:39,45.0,,1.0,3.0,7.0,1.0,,,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +379,gprep,,ml-dft,MIT,https://gitlab.com/jmargraf/gprep,Fitting DFTB repulsive potentials with GPR.,4,False,['single-paper'],,,2019-09-30 09:15:04,2019-09-30 09:15:04.000,,,,0.0,,,,,,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,jmargraf/gprep,https://gitlab.com/jmargraf/gprep,, +380,closed-loop-acceleration-benchmarks,,materials-discovery,MIT,https://github.com/aced-differentiate/closed-loop-acceleration-benchmarks,Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational..,4,False,"['materials-discovery', 'active-learning', 'single-paper']",aced-differentiate/closed-loop-acceleration-benchmarks,https://github.com/aced-differentiate/closed-loop-acceleration-benchmarks,2022-11-10 20:22:30,2023-07-25 21:25:42.000,2023-05-02 17:07:48,17.0,,1.0,4.0,3.0,,,,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +381,PeriodicPotentials,,ml-iap,MIT,https://github.com/AaltoRSE/PeriodicPotentials,A Periodic table app that displays potentials based on the selected elements.,4,False,"['community', 'visualization', 'lang-js']",AaltoRSE/PeriodicPotentials,https://github.com/AaltoRSE/PeriodicPotentials,2022-10-14 09:03:59,2022-10-18 17:10:22.000,2022-10-18 17:10:22,17.0,,1.0,3.0,3.0,,,,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +382,CatBERTa,,language-models,,https://github.com/hoon-ock/CatBERTa,Large Language Model for Catalyst Property Prediction.,3,False,"['transformer', 'catalysis']",hoon-ock/CatBERTa,https://github.com/hoon-ock/CatBERTa,2023-05-19 18:23:17,2024-03-08 02:59:22.000,2024-03-08 02:59:22,93.0,,2.0,1.0,2.0,,,19.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +383,ALEBREW,,active-learning,https://github.com/nec-research/alebrew/blob/main/LICENSE.txt,https://github.com/nec-research/alebrew,Official repository for the paper Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic..,3,False,"['ml-iap', 'md']",nec-research/alebrew,https://github.com/nec-research/alebrew,2024-02-27 07:32:23,2024-03-17 13:51:57.000,2024-03-17 13:51:52,2.0,,3.0,1.0,,1.0,,9.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +384,atom_by_atom,,rep-learn,,https://github.com/learningmatter-mit/atom_by_atom,Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with Machine Learning.,3,False,"['surface-science', 'single-paper']",learningmatter-mit/atom_by_atom,https://github.com/learningmatter-mit/atom_by_atom,2023-05-30 20:18:00,2023-10-19 15:59:08.000,2023-10-19 15:35:49,74.0,,,2.0,,,,6.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +385,DeepCDP,,ml-dft,,https://github.com/siddarthachar/deepcdp,DeepCDP: Deep learning Charge Density Prediction.,3,False,,siddarthachar/deepcdp,https://github.com/siddarthachar/deepcdp,2021-12-18 14:26:56,2023-06-16 20:38:23.000,2023-06-16 20:38:23,96.0,,1.0,2.0,27.0,,,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +386,Element encoder,,rep-learn,GPL-3.0,https://github.com/jeherr/element-encoder,Autoencoder neural network to compress properties of atomic species into a vector representation.,3,False,['single-paper'],jeherr/element-encoder,https://github.com/jeherr/element-encoder,2019-03-27 17:11:30,2020-01-09 15:54:27.000,2020-01-09 15:54:26,8.0,,2.0,4.0,,,1.0,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +387,interface-lammps-mlip-3,,md,GPL-2.0,https://gitlab.com/ivannovikov/interface-lammps-mlip-3,An interface between LAMMPS and MLIP (version 3).,3,False,,,,2023-04-24 12:48:51,2023-04-24 12:48:51.000,,,,5.0,,,4.0,1.0,5.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,ivannovikov/interface-lammps-mlip-3,https://gitlab.com/ivannovikov/interface-lammps-mlip-3,, +388,sl_discovery,,materials-discovery,Apache-2.0,https://github.com/CitrineInformatics-ERD-public/sl_discovery,Data processing and models related to Quantifying the performance of machine learning models in materials discovery.,3,False,"['materials-discovery', 'single-paper']",CitrineInformatics-ERD-public/sl_discovery,https://github.com/CitrineInformatics-ERD-public/sl_discovery,2022-10-24 18:10:14,2022-12-20 23:46:05.000,2022-12-20 23:45:57,5.0,,2.0,2.0,,,,5.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +389,APET,,ml-dft,GPL-3.0,https://github.com/emotionor/APET,Atomic Positional Embedding-based Transformer.,3,False,"['density-of-states', 'transformer']",emotionor/APET,https://github.com/emotionor/APET,2023-03-06 01:53:16,2024-04-24 03:43:39.000,2023-09-28 03:16:11,11.0,,,2.0,,,,4.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +390,e3psi,,ml-esm,LGPL-3.0,https://github.com/muhrin/e3psi,Equivariant machine learning library for learning from electronic structures.,3,False,,muhrin/e3psi,https://github.com/muhrin/e3psi,2022-08-08 10:48:30,2024-01-05 12:59:56.000,2024-01-05 12:59:09,19.0,,,2.0,,,,3.0,,,,,,,1.0,1.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +391,torch_spex,,math,,https://github.com/lab-cosmo/torch_spex,Spherical expansions in PyTorch.,3,False,,lab-cosmo/torch_spex,https://github.com/lab-cosmo/torch_spex,2023-03-28 09:48:36,2024-03-28 05:33:01.000,2024-03-28 05:33:01,77.0,,2.0,18.0,35.0,7.0,2.0,3.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +392,PiNN Lab,,educational,GPL-3.0,https://github.com/Teoroo-CMC/PiNN_lab,Material for running a lab session on atomic neural networks.,3,False,,Teoroo-CMC/PiNN_lab,https://github.com/Teoroo-CMC/PiNN_lab,2019-03-17 22:09:30,2023-05-01 15:59:56.000,2023-05-01 15:59:22,9.0,,1.0,3.0,1.0,,,2.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +393,CSNN,,ml-dft,BSD-3-Clause,https://github.com/foxjas/CSNN,Primary codebase of CSNN - Concentric Spherical Neural Network for 3D Representation Learning.,3,False,,foxjas/CSNN,https://github.com/foxjas/CSNN,2022-05-19 15:40:49,2022-10-11 04:27:40.000,2022-10-11 04:27:40,6.0,,,1.0,,,,2.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +394,Linear vs blackbox,,xai,MIT,https://github.com/CitrineInformatics-ERD-public/linear-vs-blackbox,Code and data related to the publication: Interpretable models for extrapolation in scientific machine learning.,3,False,"['xai', 'single-paper', 'rep-eng']",CitrineInformatics-ERD-public/linear-vs-blackbox,https://github.com/CitrineInformatics-ERD-public/linear-vs-blackbox,2022-12-02 20:32:53,2022-12-16 18:48:12.000,2022-12-16 18:48:12,4.0,,,1.0,,,,2.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +395,MALADA,,ml-dft,BSD-3-Clause,https://github.com/mala-project/malada,MALA Data Acquisition: Helpful tools to build data for MALA.,3,False,,mala-project/malada,https://github.com/mala-project/malada,2021-07-26 05:46:08,2024-07-26 14:19:51.000,2023-05-24 09:18:24,111.0,,1.0,2.0,4.0,17.0,2.0,1.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +396,ML-for-CurieTemp-Predictions,,rep-eng,MIT,https://github.com/msg-byu/ML-for-CurieTemp-Predictions,Machine Learning Predictions of High-Curie-Temperature Materials.,3,False,"['single-paper', 'magnetism']",msg-byu/ML-for-CurieTemp-Predictions,https://github.com/msg-byu/ML-for-CurieTemp-Predictions,2023-06-05 22:46:47,2023-06-14 19:05:50.000,2023-06-14 19:05:47,25.0,,,1.0,,,,1.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +397,Magpie,,general-tool,MIT,https://bitbucket.org/wolverton/magpie/,Materials Agnostic Platform for Informatics and Exploration (Magpie).,3,False,['lang-java'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +398,PyFLAME,,ml-iap,,https://gitlab.com/flame-code/PyFLAME,An automated approach for developing neural network interatomic potentials with FLAME..,3,False,"['active-learning', 'structure-prediction', 'structure-optimization', 'rep-eng', 'lang-fortran']",,,2021-04-07 09:16:07,2021-04-07 09:16:07.000,,,,4.0,,,,,,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,flame-code/PyFLAME,https://gitlab.com/flame-code/PyFLAME,, +399,nep-data,,datasets,,https://gitlab.com/brucefan1983/nep-data,Data related to the NEP machine-learned potential of GPUMD.,2,False,"['ml-iap', 'md', 'transport-phenomena']",,,2021-11-22 19:43:01,2021-11-22 19:43:01.000,,,,4.0,,,1.0,1.0,12.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,brucefan1983/nep-data,https://gitlab.com/brucefan1983/nep-data,, +400,SingleNN,,ml-iap,,https://github.com/lmj1029123/SingleNN,An efficient package for training and executing neural-network interatomic potentials.,2,False,['lang-cpp'],lmj1029123/SingleNN,https://github.com/lmj1029123/SingleNN,2020-03-11 18:36:16,2021-11-09 00:40:18.000,2021-11-09 00:40:10,17.0,,1.0,1.0,,1.0,,8.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +401,A3MD,,ml-dft,,https://github.com/brunocuevas/a3md,MPNN-like + Analytic Density Model = Accurate electron densities.,2,False,"['rep-learn', 'single-paper']",brunocuevas/a3md,https://github.com/brunocuevas/a3md,2021-06-02 07:23:17,2021-12-02 17:10:39.000,2021-12-02 17:10:34,4.0,,1.0,2.0,,,,8.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +402,MLDensity_tutorial,,educational,,https://github.com/bfocassio/MLDensity_tutorial,Tutorial files to work with ML for the charge density in molecules and solids.,2,False,,bfocassio/MLDensity_tutorial,https://github.com/bfocassio/MLDensity_tutorial,2023-01-31 10:33:23,2023-02-22 19:20:32.000,2023-02-22 19:20:32,8.0,,1.0,1.0,,,,7.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +403,tmQM_wB97MV Dataset,,datasets,,https://github.com/ulissigroup/tmQM_wB97MV,Code for Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV..,2,False,"['catalysis', 'rep-learn']",ulissigroup/tmqm_wB97MV,https://github.com/ulissigroup/tmQM_wB97MV,2023-07-17 21:40:20,2024-04-09 22:01:26.000,2024-04-09 22:01:26,17.0,,1.0,3.0,,,2.0,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +404,LAMMPS-style pair potentials with GAP,,educational,,https://github.com/victorprincipe/pair_potentials,A tutorial on how to create LAMMPS-style pair potentials and use them in combination with GAP potentials to run MD..,2,False,"['ml-iap', 'md', 'rep-eng']",victorprincipe/pair_potentials,https://github.com/victorprincipe/pair_potentials,2022-09-21 09:45:03,2022-10-03 08:06:22.000,2022-10-03 08:05:53,36.0,,,1.0,1.0,,,4.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +405,KmdPlus,,unsupervised,,https://github.com/Minoru938/KmdPlus,"This module contains a class for treating kernel mean descriptor (KMD), and a function for generating descriptors with..",2,False,,Minoru938/KmdPlus,https://github.com/Minoru938/KmdPlus,2023-03-26 10:06:34,2023-10-17 08:28:01.000,2023-10-17 08:28:01,7.0,,1.0,1.0,,,,3.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +406,AisNet,,ml-iap,MIT,https://github.com/loilisxka/AisNet,A Universal Interatomic Potential Neural Network with Encoded Local Environment Features..,2,False,,loilisxka/AisNet,https://github.com/loilisxka/AisNet,2022-10-11 05:54:59,2022-10-11 06:02:47.000,2022-10-11 05:58:06,2.0,,,1.0,,,,3.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +407,MALA Tutorial,,educational,,https://github.com/mala-project/mala_tutorial,A full MALA hands-on tutorial.,2,False,,mala-project/mala_tutorial,https://github.com/mala-project/mala_tutorial,2023-03-09 14:01:54,2023-11-28 11:20:39.000,2023-11-28 11:17:01,24.0,,,2.0,,,,2.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +408,Wigner Kernels,,math,,https://github.com/lab-cosmo/wigner_kernels,Collection of programs to benchmark Wigner kernels.,2,False,['benchmarking'],lab-cosmo/wigner_kernels,https://github.com/lab-cosmo/wigner_kernels,2022-12-08 12:28:26,2023-07-08 15:48:41.000,2023-07-08 15:48:37,109.0,,,1.0,,,,2.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +409,quantum-structure-ml,,general-tool,,https://github.com/hgheiberger/quantum-structure-ml,Multi-class classification model for predicting the magnetic order of magnetic structures and a binary classification..,2,False,"['magnetism', 'benchmarking']",hgheiberger/quantum-structure-ml,https://github.com/hgheiberger/quantum-structure-ml,2020-10-05 01:11:01,2022-12-22 21:45:40.000,2022-12-22 21:45:40,19.0,,,2.0,,,,2.0,2022-08-18 05:25:24.000,1.0.0,1.0,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +410,RuNNer,,ml-iap,GPL-3.0,https://www.uni-goettingen.de/de/560580.html,The RuNNer Neural Network Energy Representation is a Fortran-based framework for the construction of Behler-..,2,False,['lang-fortran'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,https://theochemgoettingen.gitlab.io/RuNNer/, +411,Point Edge Transformer,,rep-learn,CC-BY-4.0,https://zenodo.org/record/7967079,"Smooth, exact rotational symmetrization for deep learning on point clouds.",2,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +412,nnp-pre-training,,ml-iap,,https://github.com/jla-gardner/nnp-pre-training,Synthetic pre-training for neural-network interatomic potentials.,1,False,"['pretrained', 'md']",jla-gardner/nnp-pre-training,https://github.com/jla-gardner/nnp-pre-training,2023-07-12 11:58:29,2023-12-19 12:08:14.000,2023-12-19 12:08:14,11.0,,,1.0,,,,6.0,2023-12-19 12:02:35.000,1.0,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +413,SphericalNet,,rep-learn,,https://github.com/risilab/SphericalNet,Implementation of Clebsch-Gordan Networks (CGnet: https://arxiv.org/pdf/1806.09231.pdf) by GElib & cnine libraries in..,1,False,,risilab/SphericalNet,https://github.com/risilab/SphericalNet,2022-05-31 14:39:05,2022-06-07 03:57:10.000,2022-06-07 03:53:49,1.0,,,2.0,,,,3.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +414,kdft,,ml-dft,,https://gitlab.com/jmargraf/kdf,The Kernel Density Functional (KDF) code allows generating ML based DFT functionals.,1,False,,,,2020-11-07 21:50:22,2020-11-07 21:50:22.000,,,,0.0,,,,,2.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,jmargraf/kdf,https://gitlab.com/jmargraf/kdf,, +415,mag-ace,,ml-iap,,https://github.com/mttrin93/mag-ace,Magnetic ACE potential. FORTRAN interface for LAMMPS SPIN package.,1,False,"['magnetism', 'md', 'lang-fortran']",mttrin93/mag-ace,https://github.com/mttrin93/mag-ace,2023-12-26 19:00:40,2023-12-26 22:34:27.000,2023-12-26 22:34:27,7.0,,,1.0,,,,2.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +416,mlp,,ml-iap,,https://github.com/cesmix-mit/MLP,Proper orthogonal descriptors for efficient and accurate interatomic potentials...,1,False,['lang-julia'],cesmix-mit/mlp,https://github.com/cesmix-mit/MLP,2022-02-25 23:03:09,2022-10-22 19:01:45.000,2022-10-22 19:01:42,12.0,,1.0,2.0,,,,1.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +417,GitHub topic materials-informatics,,community,,https://github.com/topics/materials-informatics,GitHub topic materials-informatics.,1,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +418,MateriApps,,community,,https://ma.issp.u-tokyo.ac.jp/en/,A Portal Site of Materials Science Simulation.,1,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,, +419,Allegro-JAX,,ml-iap,,https://github.com/mariogeiger/allegro-jax,JAX implementation of the Allegro interatomic potential.,0,True,,mariogeiger/allegro-jax,https://github.com/mariogeiger/allegro-jax,2023-07-02 19:00:00,2024-04-09 18:44:30.000,2024-04-09 18:44:30,7.0,,2.0,2.0,1.0,1.0,1.0,17.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +420,Descriptor Embedding and Clustering for Atomisitic-environment Framework (DECAF),,unsupervised,,https://gitlab.mpcdf.mpg.de/klai/decaf,Provides a workflow to obtain clustering of local environments in dataset of structures.,0,False,,,,,,,41.0,,,,,,,2.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +421,MLDensity,,ml-dft,,https://github.com/StefanoSanvitoGroup/MLdensity,Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure..,0,False,,StefanoSanvitoGroup/MLdensity,https://github.com/StefanoSanvitoGroup/MLdensity,2023-01-31 20:44:45,2024-05-27 12:28:57.000,2023-02-22 19:25:51,14.0,,,2.0,,,,2.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, diff --git a/latest-changes.md b/latest-changes.md index 5b69071..f4397e2 100644 --- a/latest-changes.md +++ b/latest-changes.md @@ -1,16 +1 @@ -## 📈 Trending Up - -_Projects that have a higher project-quality score compared to the last update. There might be a variety of reasons, such as increased downloads or code activity._ - -- OPTIMADE Python tools (🥇26 · ⭐ 64 · 📈) - Tools for implementing and consuming OPTIMADE APIs in Python. MIT -- Scikit-Matter (🥈19 · ⭐ 73 · 📈) - A collection of scikit-learn compatible utilities that implement methods born out of the materials science and.. BSD-3 scikit-learn -- gpax (🥇18 · ⭐ 200 · 📈) - Gaussian Processes for Experimental Sciences. MIT probabilistic active-learning -- MLatom (🥉13 · ⭐ 36 · 📈) - AI-enhanced computational chemistry. MIT UIP ML-IAP MD ML-DFT ML-ESM transfer-learning active-learning spectroscopy structure-optimization -- MEGAN: Multi Explanation Graph Attention Student (🥉6 · ⭐ 5 · 📈) - Minimal implementation of graph attention student model architecture. MIT rep-learn - -## 📉 Trending Down - -_Projects that have a lower project-quality score compared to the last update. There might be a variety of reasons such as decreased downloads or code activity._ - -- MatBench Discovery (🥈16 · ⭐ 82 · 📉) - An evaluation framework for machine learning models simulating high-throughput materials discovery. MIT datasets benchmarking model-repository - +Nothing changed from last update. \ No newline at end of file