机器学习原子间相互作用建模

MOLECULAR MODELING BY MACHIN LEARNING

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.