Evaluation of Thermal Properties of extended 2D Materials using Gaussian Approximation Potentials

In recent years, atomically thin two-dimensional materials (2DM) have gained attention due to their flexibility and extraordinary thermal and electronic properties for technological applications. By combining density functional theory (DFT) with Boltzmann transport equation (BTE) it is possible to predict thermal transport properties accurately of these materials; however, the computational cost could be prohibitive for high-throughput calculations or for more realistic simulations with larger super-cell sizes. Herein, we trained Machine Learning potentials, based on Gaussian Approximation Potentials, using an ad-hoc reference dataset of DFT calculations to provide accurate forces for BTE model to estimate the thermal properties of 2DM structures, such as graphene, buckled silicene, and h-XN (X = B, Al, and Ga, as biatomic 2DM) structures. We validated our potentials computing phonon dispersion curves and lattice thermal conductivity via harmonic and anharmonic force constants, respectively, and compared them to DFT results. Additionally, we calculated with out method anharmonic force constants to generate high-order force constants, we found that in the case of 2nd order force constants, GAP predicted not only low-frequency acoustic modes accurately, which are the main heat carriers in semiconductors and insulators, but also relatively high-frequency optical modes. Moreover, this method enabled us to compute 3rd order force constants similar to DFT.