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机器学习引导的选有利于在金属电池中的固体聚合物电解质的溶剂

Jiadong Shen¹, Junjie Chen¹, Xiaosa Xu¹

¹Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China.

|May 2, 2025

概括

我们开发了一种机器学习模型,用于选固体聚合物电解质 (SPEs) 的溶剂. 这种方法可以确定最佳的溶剂,以提高离子导电性和电池性能,加速设计更安全,高能金属电池.

科学领域:

材料科学材料科学材料科学
电化学电化学电化学
计算化学计算化学

背景情况:

微量残留溶剂对固体聚合物电解质 (SPE) 性能产生重大影响,影响离子导电性和离子传输.
目前的溶剂选方法复杂且缺乏普遍性,阻碍了有效的SPE开发.
了解溶剂电子/宏观性质与SPE性能之间的联系至关重要.

研究的目的:

建立一个普遍的标准来选择最佳的微量溶剂在SPEs.
为了加速高能金属电池的SPE的合理设计.
开发一种基于机器学习的选方法来选择溶剂.

主要方法:

使用高通量密度函数理论 (DFT) 计算,对一组~10,000个溶剂的数据集进行计算.
开发了一种机器学习模型,将电子属性 (HOMO,LUMO) 和宏观属性 (介电常数,二极矩,极化性) 与SPE性能相关联.
在聚乙烯化物-co-hexafluoropropylene) 矩阵中实验验证有希望的溶剂候选物.

主要成果:

确定了两个新型溶剂:N-甲氧-N-甲基-2,2,2-三乙胺和2,2,2-三-N,N-二甲基乙胺.
纳入N-甲氧-N-甲基-2,2,2-三乙胺的结果是4.5V的电化学窗口,5.5 × 10−4 S cm−1的导电率和0.78.7的Li+转移数.
含有特定溶剂的SPEs表现出极好的循环稳定性,在500个循环 (LiFePO4) 上保持86.7%的容量,在200个循环 (LiNi0.9Co0.05Mn0.05O2) 上保持98.7%的容量.

结论:

综合计算和实验方法为SPEs提供了一个通用的溶剂选范式.
优化的微量溶剂使金属电池能够实现平衡的离子传输,广泛的电化学稳定性和增强的循环耐用性.
这种方法通过合理的SPE设计加速了下一代更安全的高能电池的开发.

关键词:

高通量 DFT 的计算.金属电池是金属电池的一种.机器学习算法的算法其他残留溶剂的溶剂.固体聚合物电解质的电解质.

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