期刊
NPJ COMPUTATIONAL MATERIALS
卷 7, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41524-021-00543-3
关键词
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资金
- Bosch Research and Technology Center
- ARPA-E Award [DE-AR0000775]
- Office of Science of the Department of Energy [DE-AC05-00OR22725]
- US Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD) [70NANB14H012]
- Toyota Research Institute (TRI)
GNNFF framework accurately predicts atomic forces using machine learning, achieving high performance and computational speed across various material systems, and accurately predicting the forces of large MD systems after training on smaller systems. The Li diffusion coefficient obtained through MD simulation using this framework shows good accuracy compared to AIMD.
Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant, but rotationally-covariant to the coordinate of the atoms. We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system. Finally, we use our framework to perform an MD simulation of Li7P3S11, a superionic conductor, and show that resulting Li diffusion coefficient is within 14% of that obtained directly from AIMD. The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.
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