  Thermal management materials are of critical importance for engineering
miniaturized electronic devices, where theoretical design of such materials
demands the evaluation of thermal conductivities which are numerically
expensive. In this work, we applied the recently developed machine learning
interatomic potential (MLIP) to evaluate the thermal conductivity of hexagonal
boron nitride monolayers. The MLIP is obtained using the Gaussian approximation
potential (GAP) method, and the resulting lattice dynamical properties and
thermal conductivity are compared with those obtained from explicit frozen
phonon calculations. It is observed that accurate thermal conductivity can be
obtained based on MLIP constructed with about 30% representative
configurations, and the high-order force constants provide a more reliable
benchmark on the quality of MLIP than the harmonic approximation.

Materials Science