Chen Ruoxia, Bauchy Mathieu, Wang Wei, Sun Yizhou, Tao Xiaojie, Marian Jaime
Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab), Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, 90095, USA.
Department of Computer Science, University of California, Los Angeles, CA, 90095, USA.
Sci Rep. 2025 Jul 1;15(1):22122. doi: 10.1038/s41598-025-05205-8.
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.
对无序系统进行建模的关键在于精确模拟原子轨迹，这通常通过分子动力学（MD）模拟来实现。MD模拟的准确性取决于原子间势函数的精度，该函数决定了原子运动的计算。传统上，推导原子间势函数依赖于广泛的先验物理知识和高计算成本。本研究引入了一种将机器学习与分子动力学方法相结合的新方法，以为无序系统提供精确的原子间势能计算。
