Feng Wang1,2, Yu-Hang Tang1, Ze-Bing Ma1

  • 1State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China.

Nature communications
|December 31, 2025
PubMed
概括

一个新的通用机器学习潜力准确地预测了离子电池的电解质特性. 这种计算工具通过分析离子协调动态来增强电解质设计,以提高电池性能.