循环神经网络
分子动力学
解耦(概率)
计算机科学
自相关
职位(财务)
从头算
原子单位
统计物理学
人工神经网络
化学
物理
人工智能
计算化学
数学
量子力学
控制工程
统计
财务
工程类
经济
作者
Jiaqi Wang,Chengcheng Li,SangJoon Shin,Hairong Qi
标识
DOI:10.1021/acs.jpcc.0c01944
摘要
Ab initio molecular dynamics (AIMD) is a versatile and reliable computational approach to atomic-scale materials science. However, due to the expensive computational cost on the first-principles calculation at each time step, the temporal and spatial scales are significantly limited, hindering its broader applications. Therefore, to accelerate the simulation clock of AIMD, atomic data production in AIMD using a recurrent neural network (RNN) is studied in this research. We demonstrate the feasibility of incorporating RNN-predicted time steps in AIMD, while maintaining its accuracy. The RNN models, which are trained using AIMD simulation results, directly predict atomic velocities and positions of Si atoms, reducing errors by decoupling the position and velocity update procedures from the Newtonian mechanics. Not only the predicted atomic data but also material properties calculated using the predicted data, such as the radial distribution function, temperature, velocity autocorrelation function, phonon density of states, and heat capacity, exhibit excellent agreements with the ground-truth AIMD calculations. Since the RNN prediction is much faster than the first-principles calculation of AIMD, this approach is expected to effectively accelerate AIMD, contributing to computational materials research.
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