A General-Purpose and Efficient Machine-Learned Potential for SiC from Ambient to Extreme Environments

Abstract

Silicon carbide (SiC) polymorphs are widely employed as nuclear materials, mechanical components, and wide-bandgap semiconductors. The rapid advancement of SiC-based applications has been complemented by computational modeling studies, including both ab initio and classical atomistic approaches. In this work, we develop a computationally efficient, general-purpose machine-learned interatomic potential (ML-IAP) capable of multimillion-atom molecular dynamics simulations over microsecond timescales. Using the ML-IAP, we map a broad pressure-temperature phase diagram and the threshold displacement energy distributions for the 2H and 3C polymorphs. Across a benchmark covering conditions from ambient to extreme, including high-pressure/high-temperature states and high-energy cascade damage, tabGAP provides a favorable balance of accuracy, robustness, transferability, and computational cost among the tested empirical and ML-IAPs.