  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.
