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首頁> 中文期刊> 《中國(guó)科學(xué)》 >Symmetry-adapted graph neural networks for constructing molecular dynamics force fields

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

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摘要

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.

著錄項(xiàng)

  • 來源
    《中國(guó)科學(xué)》 |2021年第11期|P.118-126|共9頁
  • 作者單位

    State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 China;

    Department of Physics Carnegie Mellon University Pittsburgh 15213 USA;

    State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 China;

    State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 China;

    State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 ChinaFrontier Science Center for Quantum Information Beijing 100084 ChinaRIKEN Center for Emergent Matter Science(CEMS) Wako Saitama 351-0198 Japan;

    State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 ChinaInstitute for Advanced Study Tsinghua University Beijing 100084 China;

    State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 ChinaInstitute for Advanced Study Tsinghua University Beijing 100084 ChinaFrontier Science Center for Quantum Information Beijing 100084 China;

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  • 中圖分類 代數(shù)、數(shù)論、組合理論;
  • 關(guān)鍵詞

    graph; neural; networks; molecular; dynamics; force; fields;

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