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Updated: Jul 14, 2025

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
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捕获BaSnF4离子导体中的相互作用:机器学习潜力和极化力场之间的比较

Xiliang Lian1,2, Mathieu Salanne1,2,3

  • 1Sorbonne Université, CNRS, Physicochimie des Électrolytes et Nanosystèmes Interfaciaux, F-75005 Paris, France.

The Journal of chemical physics
|October 10, 2023
PubMed
概括

机器学习潜力为研究化 (BaSnF4) 固体电解质中的离子流动性提供了一种高效准确的方法. 这种方法克服了传统模拟的计算局限性,为电池研究铺平了道路.

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