深度势能方法在材料科学中的应用  

Deep Potentials for Materials Science

作　　者：文通其 刘怀忆 龚小国 叶贝琳 刘思宇 李卓远 WEN Tongqi;LIU Huaiyi;GONG Xiaoguo;YE Beilin;LIU Siyu;LI Zhuoyuan(Department of Mechanical Engineering,The University of Hong Kong,Hong Kong 999077,China)

机构地区：[1]香港大学机械工程系,中国香港999077

出　　处：《金属学报》2024年第10期1299-1311,共13页Acta Metallurgica Sinica

基　　金：香港大学种子基金项目No.2201100392。

摘　　要：第一性原理计算准确但成本高昂,而建立在传统原子间势函数(力场)基础上的分子动力学模拟速率快但精度低。为了兼顾速率与准确性,机器学习(ML)势函数应运而生并得到广泛应用。深度势能(DP)为ML势的一种,近年来备受关注。本文概述了DP方法在材料科学中的应用。首先介绍了DP的理论基础,随后详细阐述了DP模型的构建和使用,并简要回顾了DP方法在多种材料体系中的应用情况。AIS-Square为DP模型的开发提供了训练数据库及工作流。之后,对比了DP模型与第一性原理计算方法及传统势函数在精度和效率上的表现。最后,对DP方法的发展前景进行了展望。Although first-principles calculations offer high precision,they are prohibitively expensive.Conversely,molecular dynamics simulations employing classical interatomic potentials,or force fields,offer quicker but less precise outcomes.To balance between computational speed and accuracy,machine learning(ML)potential functions have been developed and have gained widespread application.The deep potential(DP)method,a type of ML potential,has attracted considerable attention recently.This paper provides a comprehensive review of DP methods in materials science.It begins with an introduction to the theoretical foundation of DP,followed by a detailed exposition of the DP model development and usage.Additionally,the application of DP in various material systems is briefly reviewed.AIS-Square contributes training databases and workflows essential for developing DP models.The paper concludes by assessing the performance of DP models relative to both first-principles calculations and classical potentials in terms of accuracy and efficiency.Finally,a brief outlook on future developments trends is provided.

关 键 词：深度势能 原子模拟 机器学习势函数 神经网络 

分 类 号：TG148[一般工业技术—材料科学与工程]