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机器学习原子间相互作用建模
MOLECULAR MODELING BY MACHIN LEARNING

查看参考文献109篇

王涵 1,2  
文摘 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,因而有希望解决这一两难困境.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.
其他语种文摘 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.
来源 计算数学 ,2021,43(3):261-278 【核心库】
DOI 10.12286/jssx.j2021-0833
关键词 机器学习 ; 原子间作用势 ; 分子动力学模拟
地址

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

语种 中文
文献类型 研究性论文
ISSN 0254-7791
学科 数学
基金 国家自然科学基金
文献收藏号 CSCD:7118700

参考文献 共 109 共6页

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引证文献 2 篇

1 林博 机器学习辅助的纳米催化反应动力学研究进展 硅酸盐学报,2023,51(2):510-519
CSCD被引 0 次

2 常晓雅 机器学习势在含能材料分子模拟中的研究进展 火炸药学报,2023,46(5):361-377
CSCD被引 1 次

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