An Accurate and Efficient Machine-Learned Potential for SiC from Ambient to Extreme Environments
Abstrak
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.
Topik & Kata Kunci
Penulis (8)
Jintong Wu
Zhuang Shao
Junlei Zhao
Flyura Djurabekova
Kai Nordlund
Fredric Granberg
Qingmin Zhang
and Jesper Byggmästar
Akses Cepat
- Tahun Terbit
- 2025
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓