DeepCrysTet: A Deep Learning Approach Using Tetrahedral Mesh for Predicting Properties of Crystalline Materials

DeepCrysTet is a novel deep learning approach for predicting material properties, which uses crystal structures represented as a 3D tetrahedral mesh generated by Delaunay tetrahedralization. DeepCrysTet provides a useful framework comprising three core components: a 3D mesh generation method, mesh-based feature design, and neural network design. The overall framework of DeepCrysTet is shown below.

Table of Contents

• Dataset
• Training DeepCrysTet
• Citation

Datasets

The evaluation dataset used in the original DeepCrysTet paper is generated from the 2018.10.18 version of the Materials Project dataset. You can download the dataset below.

Dataset	Download
Materials Project (2018.10.18 version)	Link
DeepCrysTet's Supervised Data	Link

If you want to learn more about the data generation process or create your own 3D mesh dataset, more information can be found in the data folder.

Training DeepCrysTet

Environment

We use Poetry for managing our packages. To get started, clone DeepCrysTet repository and run the following command from the root directory of this repository.

poetry install --no-root

Run the following command to activate the environment:

poetry shell

Run Training

The model is trained using DeepCrysTet's supervised data by executing the following commands.

python train.py --data-path "mp-3dmesh.npz" \
  --target-path "id_prop_e_form.csv" \
  --task "regression" \
  --epochs 200 \
  --batch-size 128 \
  --amp "True"

Arguments:

Argument	Required	Default	Description
--data-path	Yes		Path of 3D mesh dataset
--target-path	Yes		Path of target variables
--task	No	"regression"	Task name ("regression" or "classification")
--es-patience	No	50	Number of patience epochs for EarlyStoppings
--save-dir	No	"./saved"	Save directory path
--epochs	No	20	Number of epochs
--batch-size	No	128	Size of mini-batch
--amp	No	False	Use Automatic Mixed Precision to save memory usage
--run-id	No		Run ID used for the directory name for saving the results
--model-path	No		Model path used for retraining

Citation

If you use DeepCrysTet in your research, please use the following citation:

@inproceedings{tsuruta2023deepcrystet,
  title={{D}eep{C}rys{T}et: A Deep Learning Approach Using Tetrahedral Mesh for Predicting Properties of Crystalline Materials},
  author={Hirofumi Tsuruta and Yukari Katsura and Masaya Kumagai},
  booktitle={2023 International Conference on Machine Learning and Applications (ICMLA)},
  year={2023},
}