Neuroevolution potential for thermal transport in silicon carbide

Abstract

Silicon carbide (SiC) is a representative high-thermal-conductivity semiconductor for applications in power electronics and quantum devices. In these applications, thermal management becomes critical for stable functioning. Meanwhile, SiC crystallizes into hundreds of polytypes with various stacking orders and small energy difference. Such a characteristic also leads to the formation of stacking faults. To understand the thermal transport property of SiC in the presence of polytypism and crystallographic defects, we have developed a neuroevolution potential for SiC. The model is trained on a diverse dataset with stoichiometric and off-stoichiometric Si_xC_y configurations. The dataset is strategically sampled using graph network encoding and clustering in the feature space. The potential achieves high accuracy with energy error of 4.1 meV/atom and force error of 0.22 eV/Å in the test set. And the dataset diversity is found to be critical to the model robustness. The application of this model is demonstrated by simulating the thermal transport property of SiC polytypes and stacking faults using homogeneous nonequilibrium molecular dynamics. The thermal resistance of stacking faults can reach substantial values up to 10^-10 K·m²·W^-1, which may create hot spots and impede heat dissipation in devices. The thermal resistance varies considerably among different fault types, with those exhibiting consecutive cubic k stacking configurations demonstrating remarkably lower resistance. This work provides an accurate and efficient machine-learning interatomic potential for simulations of thermal and phonon properties of SiC and understanding of its thermal management in the presence of polytypism and crystallographic defects.

Keywords

Silicon carbide / machine learning interatomic potential / thermal transport / stacking fault

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