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First‐Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine‐Learning Interatomic Potentials by Bohayra Mortazavi; Mohammad Silani; Evgeny V. Podryabinkin; Timon Rabczuk; Xiaoying Zhuang; Alexander V. Shapeev is a Engineering article available to read on EtoBox.

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## Abstract Density functional theory calculations are robust tools to explore the mechanical properties of pristine structures at their ground state but become exceedingly expensive for large systems at finite temperatures. Classical molecular dynamics (CMD) simulations offer the possibility to study larger systems at elevated temperatures, but they require accurate interatomic potentials. Herein the authors propose the concept of first‐principles multiscale modeling of mechanical properties, where ab initio level of accuracy is hierarchically bridged to explore the mechanical/failure response of macroscopic systems. It is demonstrated that machine‐learning interatomic potentials (MLIPs) fitted to ab initio datasets play a pivotal role in achieving this goal. To practically illustrate this novel possibility, the mechanical/failure response of graphene/borophene coplanar heterostructures is examined. It is shown that MLIPs conveniently outperform popular CMD models for graphene and borophene and they can evaluate the mechanical properties of pristine and heterostructure phases at room temperature. Based on the information provided by the MLIP‐based CMD, continuum models of heterost

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Author

Bohayra Mortazavi; Mohammad Silani; Evgeny V. Podryabinkin; Timon Rabczuk; Xiaoying Zhuang; Alexander V. Shapeev

Publisher

Wiley

Published

2021

Language

EN

Field

Engineering (Physical Sciences)