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Updated: Jan 18, 2026

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
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Published on: August 22, 2025

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通过使用有效的机器学习原子间潜力,使离子电池中固体电解质介相材料的准确建模成为可能

Wen-Qing Li1, Gang Wu1, Juan Manuel Arce-Ramos1

  • 1Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Republic of Singapore. ngmf@a-star.edu.sg.

Materials horizons
|September 12, 2025
PubMed
概括

机器学习的原子间潜力 (MLIP) 改善了离子电池中固体电解质介相 (SEI) 材料的原子模拟. 这种方法可以准确地建模SEI属性,克服传统方法的局限性.

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