Funct. Mater. 2026; 32 (1): 98-105.
Temperature dependent thermo-mechanical properties of 3C and 6H silicon carbide from atomistic simulations on large-scale systems with DFT-derived machine learning interatomic potential
Frantsevich Institute for Problems of Materials Science NASU, O. Pritsaka street 3, Kyiv, 03142, Ukraine
This work presents a comprehensive, comparative study of two primary SiC polytypes, cubic (3C) and hexagonal (6H), using large-scale molecular dynamics simulations powered by a high-fidelity Machine Learning Interatomic Potential (MLIP) trained on a custom dataset of 5152 first-principles configurations. We present a complete set of thermo-mechanical properties, including the coefficient of thermal expansion (CTE) and the full elastic tensor (Cij), from 0 K to 2000 K. A crucial finding from the analysis of the uniaxial Young′s modulus is that the 6H polytype undergoes thermal softening at a rate of approximately 49 MPa/K, which is almost twice as fast as the 3C polytype at 25 MPa/K. This pronounced single-crystal anisotropy contrasts with the behavior of the Voigt-Reuss-Hill averaged polycrystalline moduli, which show similar softening rates of ~33 MPa/K for both materials. The presented properties are validated against established experimental and theoretical data, providing a quantitative understanding of thermo-mechanical behavior behavior of SiC and delivering the essential property data needed to enhance the fidelity of engineering models for SiC components.
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