MatGL (Materials Graph Library) is a framework for graph deep learning for materials science.
Project description
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) and its predecessors, MEGNet using the Deep Graph Library (DGL). 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 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
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
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.
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