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

TL;DR

This paper introduces two deep learning-based inter-atomic potentials for silicon carbide that enable accurate and efficient molecular dynamics simulations of thermal and phonon behaviors at quantum accuracy.

Contribution

The work develops novel deep potential inter-atomic potentials for SiC, improving simulation accuracy and efficiency for thermal and phonon properties.

Findings

DP-IAPs achieve quantum accuracy in lattice dynamics

Enhanced simulation speed for phonon transport analysis

Systematic investigation of thermal and mechanical properties

Abstract

Silicon carbide (SiC) is an essential material for next generation semiconductors and components for nuclear plants. It's applications are strongly dependent on its thermal conductivity, which is highly sensitive to microstructures. Molecular dynamics (MD) simulation is the most used methods to address thermal transportation mechanisms in devices or microstructures of nano-meters. However, the implementation of MD is limited in SiC because of lacking accurate inter-atomic potentials. In this work, using the Deep Potential (DP) methodology, we developed two inter-atomic potentials (DP-IAPs) for SiC based on two adaptively generated datasets within the density functional approximations at the local density and the generalized gradient levels. These two DP-IAPs manifest their speed with quantum accuracy in lattice dynamics simulations as well as scattering rate analysis of phonon transportation. Combining with molecular dynamics simulations, the thermal transport and mechanical properties were systematically investigated. The presented methodology and the inter-atomic potentials pave the way for a systematic approach to model heat transport in SiC related devices using multiscale modelling.