Machine Learning Interatomic Potential for Anisotropic Thermal Transport in Bulk Hexagonal Boron Nitride

TL;DR

This paper develops a machine learning interatomic potential for bulk hexagonal boron nitride that accurately predicts anisotropic thermal transport properties with high efficiency, surpassing traditional methods.

Contribution

The study introduces a Gaussian approximation potential for layered h-BN that accurately models anisotropic phonon transport and thermal conductivity at DFT-level accuracy with reduced computational cost.

Findings

GAP accurately predicts anisotropic thermal conductivity of h-BN.

The potential reproduces subtle features of the potential energy surface.

MLIPs can significantly enhance understanding of phonon transport in layered materials.

Abstract

The highly anisotropic thermal conductivity in layered materials is crucial for a broad range of applications such as thermal management of electronic devices, thermal insulation, and thermoelectrics. Understanding of anisotropic thermal transport in layered materials largely depends on atomistic simulations based on density functional theory (DFT) or empirical potentials, which however suffer either low computational efficiency or accuracy. Recently, machine learning interatomic potentials (MLIPs) are emerging as a powerful tool to bridge the gap. Despite the recent progress in developing MLIPs, little attention has been paid to constructing a potential that can accurately predict the thermal properties of layered materials, which is more challenging compared with the case of isotropic materials because of the highly anisotropic bonding and weak van der Waals interactions in layered materials. Here, we introduce a MLIP within the Gaussian approximation potential (GAP) framework for bulk hexagonal boron nitride (h-BN) with a typical layered structure. The GAP can well predict the highly anisotropic phonon transport properties and thermal conductivity of bulk h-BN with DFT-level accuracy at orders of magnitude reduced cost. Our work demonstrates the ability of GAP to reproduce the subtle features of anisotropic potential energy surfaces of bulk h-BN and potentially other layered materials. Atomistic simulations based on MLIPs are expected to be able to greatly promote the understanding of phonon transport and the prediction of thermophysical properties in layered materials.