Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures

authored by

Bohayra Mortazavi, Evgeny V. Podryabinkin, Stephan Roche, Timon Rabczuk, Xiaoying Zhuang, Alexander V. Shapeev

Abstract

One of the ultimate goals of computational modeling in condensed matter is to be able to accurately compute materials properties with minimal empirical information. First-principles approaches such as density functional theory (DFT) provide the best possible accuracy on electronic properties but they are limited to systems up to a few hundreds, or at most thousands of atoms. On the other hand, classical molecular dynamics (CMD) simulations and the finite element method (FEM) are extensively employed to study larger and more realistic systems, but conversely depend on empirical information. Here, we show that machine-learning interatomic potentials (MLIPs) trained over short ab initio molecular dynamics trajectories enable first-principles multiscale modeling, in which DFT simulations can be hierarchically bridged to efficiently simulate macroscopic structures. As a case study, we analyze the lattice thermal conductivity of coplanar graphene/borophene heterostructures, recently synthesized experimentally (Sci. Adv., 2019, 5, eaax6444), for which no viable classical modeling alternative is presently available. Our MLIP-based approach can efficiently predict the lattice thermal conductivity of graphene and borophene pristine phases, the thermal conductance of complex graphene/borophene interfaces and subsequently enable the study of effective thermal transport along the heterostructures at continuum level. This work highlights that MLIPs can be effectively and conveniently employed to enable first-principles multiscale modeling via hierarchical employment of DFT/CMD/FEM simulations, thus expanding the capability for computational design of novel nanostructures.

Organisation(s)

PhoenixD: Photonics, Optics, and Engineering - Innovation Across Disciplines
Institute of Photonics

External Organisation(s)

Skolkovo Institute of Science and Technology
ICN - Catalan Institute of Nanotechnology
Catalan Institution for Research and Advanced Studies (ICREA)
Tongji University

Type

Article

Journal

Materials Horizons

Volume

7

Pages

2359-2367

No. of pages

9

ISSN

2051-6347

Publication date

09.2020

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Materials Science(all), Mechanics of Materials, Process Chemistry and Technology, Electrical and Electronic Engineering

Electronic version(s)

https://doi.org/10.1039/d0mh00787k (Access: Closed)