Using graph neural network and symbolic regression to model disordered systems.

Journal: Scientific reports
Published Date: Jul 1, 2025

Abstract

The key to modeling disordered systems lies in accurately simulating atomic trajectories, typically achieved through molecular dynamic (MD) simulation. The accuracy of MD simulations depends on the precision of the interatomic potential function, which dictates the calculations of atom movements. Traditionally, deriving interatomic potential function relies on extensive prior physical knowledge and high computational cost. This study introduces a novel approach that integrates machine learning with molecular dynamic methods to provide precise interatomic potential energy calculations for disordered systems.

Authors

  • Ruoxia Chen
    Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab), Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, 90095, USA. ruoxia@g.ucla.edu.
  • Mathieu Bauchy
    Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab), Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, 90095, USA.
  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
  • Yizhou Sun
    College of Computer and Information Science, Northeastern University, 360 Huntington Avenue, Boston, MA, USA.
  • Xiaojie Tao
    Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Los Angeles, CA, 90095, USA.
  • Jaime Marian
    Department of Materials Science and Engineering, University of California, Los Angeles, CA, 90095, USA. jmarian@g.ucla.edu.

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