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• W3049186827 abstract "Machine learning force field (ML-FF) has emerged as a potential promising approach to simulate various material phenomena for large systems with ab initio accuracy. However, most ML-FFs have been used to study the phenomena relatively close to the equilibrium ground states. In this work, we have studied a far from equilibrium system of liquid to crystal Si growth using ML-FF. We found that our ML-FF based on ab initio decomposed atomic energy can reproduce all the aspects of ab initio simulated growth, from local energy fluctuations to transition temperatures, to diffusion constant, and growth rates. We have also compared the growth simulation with the Stillinger-Weber classical force field and found significant differences. A procedure is also provided to correct a systematic fitting bias in the ML-FF training process, which exists in all training models, otherwise critical results like transition temperature will be wrong." @default.
• W3049186827 created "2020-08-21" @default.
• W3049186827 creator A5041867414 @default.
• W3049186827 creator A5091144208 @default.
• W3049186827 date "2020-08-17" @default.
• W3049186827 modified "2023-10-13" @default.
• W3049186827 title "Liquid to crystal Si growth simulation using machine learning force field" @default.
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• W3049186827 doi "https://doi.org/10.1063/5.0011163" @default.
• W3049186827 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32828094" @default.
• W3049186827 hasPublicationYear "2020" @default.
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