机器学习原子间相互作用建模

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.

MOLECULAR MODELING BY MACHIN LEARNING

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.