Abstract
AbstractThis 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.
Highlights
Advanced manufacturing using new materials and emerging technologies encompasses all the aspects of industrial production
machine learning (ML) promotes the application of molecular dynamics (MD) simulation in terms of two aspects
The possible synthetic route of chemicals based on the results of deep neural networks has a similar feasibility to those formulated by human experts.[10]
Summary
Advanced manufacturing using new materials and emerging technologies encompasses all the aspects of industrial production. In many fields, such as microelectronics and nanoelectronics, the cost of developing and producing the tools for manufacture has become a major limitation for the whole system. ML promotes the application of MD simulation in terms of two aspects It can accelerate the development of traditional empirical potential functions as well as directly represent MD systems by using ML potentials. The machine‐learning‐based interatomic potential has already been developed and is widely used in various fields of materials research.
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