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Fast and easy fingerprints computation of atomic and molecular systems for machine learning studies.

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Documentation Status

SEING v0.1

SEING is a C/C++ package for fingerprint calculations for machine learning studies of atomic and molecular systems.

SEING was developed in the Clancy Group (http://clancygroup.cbe.cornell.edu/) at Cornell University.

Author: Mardochee Reveil ([email protected])

Fingerprints (in this context) are numerical representations of chemical environments designed to be invariant under property-perseving operations such as permutation of atoms of the same nature, geometric rotation, etc. For more information on fingerprints in general and the ones currently implemented in SEING, please see the official documentation and user-guide.

"SEING" is an old French word for signature.

DOCUMENTATION

The official documentation and user-guide can be found here: https://seing.readthedocs.io

INSTALLATION

SEING is built with minimal requirements and can be easily compiled with a suitable C/C++ compiler. A generic Makefile is provided in src folder. As a starting point, you can just type.

cd seing
mkdir bin
cd src
make seing

If this doesn't work, changes might be necessary to adapt the makefile to your operating system and/or environment.

Please note that c++11 or later is required. I have successfully compiled with GCC 4.8.4 and 4.9.2 on CentOS. If you are using GCC and run into C++ standard related errors, try adding -std=c++11 to your compile command. I will look into enabling compatibility with earlier C++ standards in future releases.

BUILD THE DOCUMENTATION

Doxygen is used to automatically generate documentation for the C++ code.

To regenerate the documentation, please install doxygen and run:

cd src
doxygen Doxyfile

This will generate html and xml documentation files and save them under doc/doxygen/.

For instructions on how to install doxygen, visit: https://www.doxygen.nl/manual/install.html

We use Sphinx to generate the documentation website hosted on https://seing.readthedocs.io

We use breathe to link Doxygen and Sphinx: https://github.com/michaeljones/breathe

To generate the site locally, start by installing Sphinx (Find instructions here https://www.sphinx-doc.org/en/master/usage/installation.html)

Then do the following (assuming you already have Python and pip installed):

pip install breathe
cd doc
make html

To update the documentation itself, modify the rst files found in doc/source/.

LICENSE

This program is free and open-source software distributed under the terms of the GNU GPL version 3 (or later) which can be found here: www.gnu.org/licenses/gpl-3.0.en.html

Please note that SEING is provided WITHOUT WARRANTY OF ANY KIND, either expressed or implied, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose. Please see the full terms of the GNU GPL license for more details.

CONTRIBUTIONS

We welcome contributions to this project including implementation of new fingerprinting schemes, bug tracking and corrections, code optimization, documentation, etc. Please consult the "developer" section of the documentation for more information on how the code is organized. To make a contribution, create your own branch, make your documented changes to the code and submit a pull request for code update.

USER SUPPORT

SEING is provided with no dedicated user support, however questions and suggestions are welcome and the author(s) will do their best to provide answers in a timely fashion.

CITATION

If you use this software in your research, please cite the appropriate paper(s) for your chosen fingerprinting method(s) as well as the official SEING paper published here: http://pubs.rsc.org/en/content/articlelanding/2018/me/c8me00003d#!divAbstract

Mardochee Reveil and Paulette Clancy, "Classification of spatially resolved molecular fingerprints for machine learning applications and development of a codebase for their implementation." Molecular Systems Design & Engineering, published online 2/20/18. Invited paper, part of a themed issue on ‘Machine Learning and Data Science in Materials Design’ DOI: 10.1039/C8ME00003D

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