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 and general-purpose machine-learned interatomic potential (ML-IAP) capable of multimillion-atom molecular dynamics simulations over microsecond timescales. Using the ML-IAP, we systematically map the comprehensive pressure-temperature phase diagram and the threshold displacement energy distributions for the 2H and 3C polymorphs. Furthermore, collision cascade simulations provide in-depth insights into polymorph-dependent primary radiation damage clustering, a phenomenon that conventional empirical potentials fail to accurately capture.
@article{arxiv.2510.01827,
title = {An Accurate and Efficient Machine-Learned Potential for SiC from Ambient to Extreme Environments},
author = {Jintong Wu and Zhuang Shao and Junlei Zhao and Flyura Djurabekova and Kai Nordlund and Fredric Granberg and Qingmin Zhang and and Jesper Byggmästar},
journal= {arXiv preprint arXiv:2510.01827},
year = {2025}
}
Comments
13 pages, 7 figures; the supplementary material will be published with the final version of the paper