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Silicon carbide (SiC) is an essential material for next generationsemiconductors and components for nuclear plants. It's applications arestrongly dependent on its thermal conductivity, which is highly sensitive tomicrostructures. Molecular dynamics (MD) simulation is the most used methods toaddress thermal transportation mechanisms in devices or microstructures ofnano-meters. However, the implementation of MD is limited in SiC because oflacking accurate inter-atomic potentials. In this work, using the DeepPotential (DP) methodology, we developed two inter-atomic potentials (DP-IAPs)for SiC based on two adaptively generated datasets within the densityfunctional approximations at the local density and the generalized gradientlevels. These two DP-IAPs manifest their speed with quantum accuracy in latticedynamics simulations as well as scattering rate analysis of phonontransportation. Combining with molecular dynamics simulations, the thermaltransport and mechanical properties were systematically investigated. Thepresented methodology and the inter-atomic potentials pave the way for asystematic approach to model heat transport in SiC related devices usingmultiscale modelling.

Deep Learning Inter-atomic Potential for Thermal and Phonon Behaviour of Silicon Carbide with Quantum Accuracy