摘要
This paper summarizes the progress of machine-learning-based interatomic potentials and their applications in advanced manufacturing.Interatomic potential is essential for classical molecular dynamics.The advancements made in machine learning(ML)have enabled the development of fast interatomic potential with ab initio accuracy.The accelerated atomic simulation can greatly transform the design principle of manufacturing technology.The most widely used supervised and unsupervised ML methods are summarized and compared.Then,the emerging interatomic models based on ML are discussed:Gaussian approximation potential,spectral neighbor analysis potential,deep potential molecular dynamics,SCHNET,hierarchically interacting particle neural network,and fast learning of atomistic rare events.
基金
This study was supported by the Wuhan University Junior Faculty Research(2042019KF0003)
the National Natural Science Foundation of China(51727901,U1501241,and 62174122)
the National Key R&D Program of China(2017YFB1103904)
the Hubei Provincial Natural Science Foundation of China(2020CFA032).