Issue 6, 2023

Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials

Abstract

Since the birth of the concept of machine learning interatomic potentials (MLIPs) in 2007, a growing interest has been developed in the replacement of empirical interatomic potentials (EIPs) with MLIPs, in order to conduct more accurate and reliable molecular dynamics calculations. As an exciting novel progress, in the last couple of years the applications of MLIPs have been extended towards the analysis of mechanical and failure responses, providing novel opportunities not heretofore efficiently achievable, neither by EIPs nor by density functional theory (DFT) calculations. In this minireview, we first briefly discuss the basic concepts of MLIPs and outline popular strategies for developing a MLIP. Next, by considering several examples of recent studies, the robustness of MLIPs in the analysis of the mechanical properties will be highlighted, and their advantages over EIP and DFT methods will be emphasized. MLIPs furthermore offer astonishing capabilities to combine the robustness of the DFT method with continuum mechanics, enabling the first-principles multiscale modeling of mechanical properties of nanostructures at the continuum level. Last but not least, the common challenges of MLIP-based molecular dynamics simulations of mechanical properties are outlined and suggestions for future investigations are proposed.

Graphical abstract: Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials

Article information

Article type
Review Article
Submitted
30 Janv. 2023
Accepted
22 Marts 2023
First published
04 Apr. 2023
This article is Open Access
Creative Commons BY-NC license

Mater. Horiz., 2023,10, 1956-1968

Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials

B. Mortazavi, X. Zhuang, T. Rabczuk and A. V. Shapeev, Mater. Horiz., 2023, 10, 1956 DOI: 10.1039/D3MH00125C

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