Details
| Original language | English |
|---|---|
| Article number | 2102807 |
| Journal | Advanced materials |
| Volume | 33 |
| Issue number | 35 |
| Early online date | 23 Jul 2021 |
| Publication status | Published - 2 Sept 2021 |
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.
Keywords
- first-principles calculations, machine learning, mechanical/failure response, multiscale modeling
ASJC Scopus subject areas
- Materials Science(all)
- General Materials Science
- Engineering(all)
- Mechanics of Materials
- Engineering(all)
- Mechanical Engineering
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In: Advanced materials, Vol. 33, No. 35, 2102807, 02.09.2021.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - First-Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine-Learning Interatomic Potentials
AU - Mortazavi, Bohayra
AU - Silani, Mohammad
AU - Podryabinkin, Evgeny V.
AU - Rabczuk, Timon
AU - Zhuang, Xiaoying
AU - Shapeev, Alexander V.
N1 - Funding Information: M.S. and E.V.P. contributed equally to this work. B.M. and X.Z. appreciate the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). E.V.P and A.V.S. were supported by the Russian Science Foundation (Grant No 18-13-00479, https://rscf.ru/project/18-13-00479/). M.S. thanks the financial support of the Iran National Science Foundation (INSF) under project no. 940004. X.Z. acknowledges the sponsorship from the European Research Council Starting Grant COTOFLEXI (No. 802205). B.M. and T.R. are greatly thankful to the VEGAS cluster at Bauhaus University of Weimar for providing the computational resources. Open Access funding enabled and organized by Projekt DEAL.
PY - 2021/9/2
Y1 - 2021/9/2
N2 - 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.
AB - 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.
KW - first-principles calculations
KW - machine learning
KW - mechanical/failure response
KW - multiscale modeling
UR - http://www.scopus.com/inward/record.url?scp=85111038766&partnerID=8YFLogxK
U2 - 10.1002/adma.202102807
DO - 10.1002/adma.202102807
M3 - Article
C2 - 34296779
AN - SCOPUS:85111038766
VL - 33
JO - Advanced materials
JF - Advanced materials
SN - 0935-9648
IS - 35
M1 - 2102807
ER -