机器学习原子间相互作用建模  

MOLECULAR MODELING BY MACHIN LEARNING

作　　者：王涵 Wang Han(Laboratory of Computational Physics,Institute of Applied Physics and Computational Mathematics,Fenghao East Road 2,Beijing 100094,China;HEDPS,CAPT,Peking University,Beijing 100871,China)

机构地区：[1]北京应用物理与计算数学研究所计算物理实验室,北京100094 [2]北京大学工学院应用物理与技术中心,北京100871

出　　处：《计算数学》2021年第3期261-278,共18页Mathematica Numerica Sinica

基　　金：国家自然科学基金(11871110)资助。

摘　　要：原子间相互作用建模是分子动力学模拟的核心问题之一.基于第一性原理的建模准而不快,经验势模型快而不准.因此人们长期面临精度和效率只得其一的两难困境.基于机器学习的原子间相互作用建模在达到第一性原理精度的同时,计算开销大大降低,因而有希望解决这两难困境.本文将介绍构造基于机器学习的原子间相互作用模型的一般框架,归纳近年来的主要建模工作,并探讨这些工作的优势和劣势.Modeling the interatomic potential is one of the crucial problems in the field of molecular simulation.For a long time,the community faces the dilemma that the first-principles calculations are accurate but slow,while the empirical force fields are efficient but inaccurate.Machine learning is a promising approach to solve the dilemma because it achieves comparable accuracy with the first-principles calculations at a much lower expense.In this review,we present a general framework for developing the machine learning interatomic potentials,provide an incomplete list of recent work in this direction,and investigate the advantages and disadvantages of the reviewed approaches.

关 键 词：机器学习 原子间作用势 分子动力学模拟 

分 类 号：O562.4[理学—原子与分子物理] TP181[理学—物理]