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

• 作 者: Zun Wang Chong Wang SiBo Zhao ShiQiao Du Yong Xu Bing-Lin Gu WenHui Duan
• 机 构: State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics,Tsinghua Universit
• 出 处: 中国科学：物理学 力学 天文学（英文版）
• 发表日期: 2021年11期
• 关键词: graph neural networks molecular dynamics force fields

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 molec-ular 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.