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机器学习在开发用于离子电池的固态电解质方面的应用和进展

Tiantian Gao^1,2, Yongliang Wu³

¹School of Chemistry and Chemical Engineering, North University of China, Taiyuan 030051, PR China.

|December 22, 2025

概括

机器学习 (ML) 加快了用于更安全的离子电池 (LIB) 固态电解质 (SSE) 的发现. 这次审查强调了ML的ML.

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:

固态电解质 (SSEs) 通过抑制树突的生长来提高离子电池 (LIB) 的安全性至关重要.
对SSE的商业化挑战包括低离子导电性,机械强度差以及接口问题.
机器学习 (ML) 提供强大的数据处理和模式识别,以加速SSE研究.

研究的目的:

审查最近在将ML技术应用于开发LIBs的SSE方面的进展.
为 ML 驱动的 SSE 发现和设计提供全面的视角.
在寻找下一代 SSE 材料时促进 ML 的整合.

主要方法:

讨论创建强大的SSE数据库的策略.
分析描述符选择对ML模型预测性能的影响.
突出了对关键SSE属性的预测和生成ML模型的应用.

主要成果:

ML模型可以有效地预测关键的SSE属性,如离子导电性,弹性模块和热力学稳定性.
系统分析和对ML模型解释性和评估指标的比较.
识别描述符选择作为模型准确性的关键因素.

结论:

ML是克服SSE限制和加速先进电池材料设计的变革性工具.
进一步整合ML对于快速发现和优化SSEs至关重要.
本综述提供了利用ML在固态电池研究领域的路线图.

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