Issue 3, 2021

Accelerating atomistic simulations with piecewise machine-learned ab Initio potentials at a classical force field-like cost

Abstract

Recently, machine learning methods have become easy-to-use tools for constructing high-dimensional interatomic potentials with ab initio accuracy. Although machine-learned interatomic potentials are generally orders of magnitude faster than first-principles calculations, they remain much slower than classical force fields, at the price of using more complex structural descriptors. To bridge this efficiency gap, we propose an embedded atom neural network approach with simple piecewise switching function-based descriptors, resulting in a favorable linear scaling with the number of neighbor atoms. Numerical examples validate that this piecewise machine-learning model can be over an order of magnitude faster than various popular machine-learned potentials with comparable accuracy for both metallic and covalent materials, approaching the speed of the fastest embedded atom method (i.e. several μs per atom per CPU core). The extreme efficiency of this approach promises its potential in first-principles atomistic simulations of very large systems and/or in a long timescale.

Graphical abstract: Accelerating atomistic simulations with piecewise machine-learned ab Initio potentials at a classical force field-like cost

Supplementary files

Article information

Article type
Paper
Submitted
26 9 2020
Accepted
16 11 2020
First published
16 11 2020

Phys. Chem. Chem. Phys., 2021,23, 1815-1821

Accelerating atomistic simulations with piecewise machine-learned ab Initio potentials at a classical force field-like cost

Y. Zhang, C. Hu and B. Jiang, Phys. Chem. Chem. Phys., 2021, 23, 1815 DOI: 10.1039/D0CP05089J

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements