Phonon and Thermal Properties of Silicon Carbide: A Comparison of Empirical and Machine Learning Potentials

J Zhang and HC Zhang and Y Zhang and XK Ma and WF Li and G Zhang, PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS, 261 (2024).

DOI: 10.1002/pssb.202400070

Silicon carbide (SiC), as a third-generation semiconductor material, has attracted significant research attention. Various empirical potentials and machine learning potentials have been developed, but there are few comparative studies on phonon and thermal properties. Herein, the Tersoff and Vashishta empirical potentials, as well as the Bayesian force field constructed by the FLARE framework using principled Gaussian process uncertainties (FLARE BFF), for a comparative study, are selected. The phonon dispersion relation, phonon density of states, Gr & uuml;neisen constants, and the average phonon-weighted Gr & uuml;neisen constants are calculated using different potentials, and it is found that the FLARE BFF potential has the highest accuracy with respect to the first-principles calculations. Furthermore, the thermal conductivity using molecular dynamics simulation with different potentials is calculated. The calculation results using the FLARE BFF potential closely match the experimental reports at high temperature, but the longest computing time is required. This study can facilitate the understanding of thermal properties of SiC. Compared to the Tersoff and Vashishta empirical potentials, the FLARE BFF potential has the highest accuracy with respect to the first-principles calculations.image (c) 2024 WILEY-VCH GmbH

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