An Accurate and Efficient Machine-Learned Potential for SiC from Ambient to Extreme Environments
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918532937154560 |
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| author | Wu, Jintong Shao, Zhuang Zhao, Junlei Djurabekova, Flyura Nordlund, Kai Granberg, Fredric Zhang, Qingmin Byggmästar, and Jesper |
| author_facet | Wu, Jintong Shao, Zhuang Zhao, Junlei Djurabekova, Flyura Nordlund, Kai Granberg, Fredric Zhang, Qingmin Byggmästar, and Jesper |
| contents | 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. Across a comprehensive benchmark covering conditions from ambient to extreme, including high-pressure/high-temperature states and high-energy cascade damage, tabGAP shows the best overall performance among ML and empirical IAPs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01827 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Accurate and Efficient Machine-Learned Potential for SiC from Ambient to Extreme Environments Wu, Jintong Shao, Zhuang Zhao, Junlei Djurabekova, Flyura Nordlund, Kai Granberg, Fredric Zhang, Qingmin Byggmästar, and Jesper Materials Science 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. Across a comprehensive benchmark covering conditions from ambient to extreme, including high-pressure/high-temperature states and high-energy cascade damage, tabGAP shows the best overall performance among ML and empirical IAPs. |
| title | An Accurate and Efficient Machine-Learned Potential for SiC from Ambient to Extreme Environments |
| topic | Materials Science |
| url | https://arxiv.org/abs/2510.01827 |