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

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 the 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 finite element method (FEM) are extensively employed to study larger and more realistic systems, but conversely depend on empirical information. Here, we show that machinelearning 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 firstprinciples multiscale modeling via hierarchical employment of DFT/CMD/FEM simulations, thus expanding the capability for computational design of novel nanostructures.

Fe/Sb2Te3 Interface Reconstruction through Mild Thermal Annealing

“…We hope our results will further boost the use of first‐principles multiscale modeling in this sense. [ 26 ]…”

First‐Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine‐Learning Interatomic Potentials

“…More importantly, MLIPs offer a unique possibility to conduct first‐principles multiscale modeling, in which ab initio level of accuracy can be hierarchically bridged to explore the mechanical/failure response of macroscopic systems. In our earlier study, [ 35 ] we show that MLIPs can be used to conduct first‐principles multiscale modeling of lattice thermal conductivity. In this work, we step forward and propose the more challenging concept of first‐principles multiscale modeling of mechanical/failure properties.…”

Nanostructured and Heterostructured 2D Materials for Thermoelectrics

“…For the materials genome, a highthroughput calculation method is required and this can be achieved with machine learning. Successful examples for machine learning in materials search and design can be found for interfacial thermal conductance, [175], [255], [256] bandgap, [257] and interatomic force constants [258], [259], [260] used in MD simulations. Machine learning driven by experimental data is desired for thermoelectric studies but is still lacking due to the challenge of high-throughput measurements at the nanoscale.…”