Metadata-Version: 2.1
Name: matgl
Version: 0.1.0.dev0
Summary: MatGL (Materials Graph Library) is a framework for graph deep learning for materials science. 
Author: Tsz Wai Ko, Marcel Nassar, Ji Qi, Santiago Miret, Shyue Ping Ong
Author-email: ongsp@eng.ucsd.edu
Maintainer: Shyue Ping Ong
Maintainer-email: ongsp@eng.ucsd.edu
Keywords: materials,interatomic potential,force field,science,property prediction,AI,machine learning,graph,deep learning
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
License-File: LICENSE

# Introduction

MatGL (Materials Graph Library) is a graph deep learning library for materials. Mathematical graphs are a natural
representation for a collection of atoms (e.g., molecules or crystals). Graph deep learning models have been shown 
to consistently deliver exceptional performance as surrogate models for the prediction of materials properties.

In this repository, we have reimplemented the [3-body MatErials Graph Network (m3gnet)](https://github.com/materialsvirtuallab/m3gnet)
and its predecessors, [MEGNet](https://github.com/materialsvirtuallab/megnet) using the [Deep Graph Library (DGL)](https://www.dgl.ai).
The goal is to improve the usability, extensibility and scalability of these models. The original M3GNet and MEGNet were
implemented in TensorFlow.

This effort is a collaboration between the [Materials Virtual Lab](http://materialsvirtuallab.org) and Intel Labs
(Santiago Miret, Marcel Nassar, Carmelo Gonzales).

# Status

Feb 16 2023: Both initial implementations of M3GNet and MEGNet architectures have been completed. Expect bugs!

# References

Please cite the following works:

- MEGNET
    ```txt
    Chen, C.; Ye, W.; Zuo, Y.; Zheng, C.; Ong, S. P. Graph Networks as a Universal Machine Learning Framework for
    Molecules and Crystals. Chem. Mater. 2019, 31 (9), 3564–3572. https://doi.org/10.1021/acs.chemmater.9b01294.
    ```
- M3GNet
    ```txt
    Chen, C., Ong, S.P. A universal graph deep learning interatomic potential for the periodic table. Nat Comput Sci,
    2, 718–728 (2022). https://doi.org/10.1038/s43588-022-00349-3.
    ```

# Acknowledgements

This work was primarily supported by the Materials Project, funded by the U.S. Department of Energy, Office of Science,
Office of Basic Energy Sciences, Materials Sciences and Engineering Division under contract no.
DE-AC02-05-CH11231: Materials Project program KC23MP. This work used the Expanse supercomputing cluster at the Extreme
Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number
ACI-1548562.
