First‐Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine‐Learning Interatomic Potentials. Issue 35 (23rd July 2021)

• Record Type: Journal Article
• Authors: Mortazavi, Bohayra; Silani, Mohammad; Podryabinkin, Evgeny V.; Rabczuk, Timon; Zhuang, Xiaoying; Shapeev, Alexander V.
• Is Part Of: Advanced materials. Volume 33:Issue 35(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-23
• Subjects: first‐principles calculations -- machine learning -- mechanical/failure response -- multiscale modeling; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adma.202102807
• Languages: English
• ISSNs: 0935-9648

Abstract

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 heterostructures using the finite element method can be constructed. The study highlights that MLIPs were the missing block for conducting first‐principles multiscale modeling, and their employment empowers a straightforward route to bridge ab initio level accuracy and flexibility to explore the mechanical/failure response of nanostructures at continuum scale. Abstract : A robust concept of first‐principles multiscale modeling of mechanical properties based on machine‐learning interatomic potentials conveniently trainable over short ab initio datasets is proposed. It is shown that mechanical/failure responses of complex nanostructures at continuum scale and high temperatures can be explored with the precision of sophisticated first‐principles calculations, affordable computational cost, and without the need for empirical data.