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Symmetry-adapted graph neural networks for constructing molecular dynamics force fields

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Abstract

Molecular dynamics is a powerful simulation tool to explore material properties. Most realistic material systems are too large to be simulated using first-principles molecular dynamics. Classical molecular dynamics has a lower computational cost but requires accurate force fields to achieve chemical accuracy. In this work, we develop a symmetry-adapted graph neural network framework called the molecular dynamics graph neural network (MDGNN) to construct force fields automatically for molecular dynamics simulations for both molecules and crystals. This architecture consistently preserves translation, rotation, and permutation invariance in the simulations. We also propose a new feature engineering method that includes high-order terms of interatomic distances and demonstrate that the MDGNN accurately reproduces the results of both classical and first-principles molecular dynamics. In addition, we demonstrate that force fields constructed by the proposed model have good transferability. The MDGNN is thus an efficient and promising option for performing molecular dynamics simulations of large-scale systems with high accuracy.

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Authors and Affiliations

  1. State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing, 100084, China

    Zun Wang, SiBo Zhao, ShiQiao Du, Yong Xu, Bing-Lin Gu & WenHui Duan

  2. Department of Physics, Carnegie Mellon University, Pittsburgh, 15213, USA

    Chong Wang

  3. Institute for Advanced Study, Tsinghua University, Beijing, 100084, China

    Bing-Lin Gu & WenHui Duan

  4. Frontier Science Center for Quantum Information, Beijing, 100084, China

    Yong Xu & WenHui Duan

  5. RIKEN Center for Emergent Matter Science (CEMS), Wako, Saitama, 351-0198, Japan

    Yong Xu

Authors
  1. Zun Wang
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  2. Chong Wang
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  3. SiBo Zhao
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  4. ShiQiao Du
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  7. WenHui Duan
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Correspondence to Chong Wang or WenHui Duan.

Additional information

This work was supported by the Basic Science Center Project of National Natural Science Foundation of China (Grant No. 51788104), the Ministry of Science and Technology of China (Grant Nos. 2016YFA0301001, and 2017YFB0701502), and the Beijing Advanced Innovation Center for Materials Genome Engineering.

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Wang, Z., Wang, C., Zhao, S. et al. Symmetry-adapted graph neural networks for constructing molecular dynamics force fields. Sci. China Phys. Mech. Astron. 64, 117211 (2021). https://doi.org/10.1007/s11433-021-1739-4

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