Determining the thermal conductivity and phonon behavior of SiC materials with quantum accuracy via deep learning interatomic potential model

SiC is essential for next-generation semiconductors and nuclear plant components. Its performance is strongly influenced by its thermal conductivity, which is highly sensitive to its microstructure. Molecular dynamics (MD) simulation is one of the most reliable methods for studying thermal transportation mechanisms in devices with nano-scale microstructures. Nevertheless, owing to inaccurate interatomic potentials, the implementation of MD for studying SiC still presents limitations. This study used the deep potential (DP) methodology to develop two interatomic potential (DP-IAPs) models for studying SiC, based on two adaptively generated datasets within the density functional approximations at the local density and generalized gradient levels. Combined with the LD and MD simulations, the thermal transport and mechanical properties of SiC were systematically investigated. The proposed DP-IAPs can accurately reproduce the structural properties, phonon behaviors, and thermal properties of various SiC polytypes. The two DP-IAPs exhibited extraordinary speed with high accuracy in both simulating the lattice dynamics (LD) and analyzing the scattering rate of phonon transportation. Our proposed methodology paves the way for a systematic approach to model heat transport in SiC-based devices and nuclear grade SiC components using multiscale modeling.