通过智能化选方法对非水性宽温度电解质进行分子设计
Tian Qin1,2, Haoyi Yang1, Lei Wang1
1Beijing Frontier Research Center on Clean Energy, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China.
Angewandte Chemie (International ed. in English)
|June 27, 2024
概括
本研究介绍了一种人工智能驱动的方法,用于设计用于离子电池的宽温度电解质 (WTE). 这种新型电解质能够使电池在-60-120°C的温度下稳定运行,这对于可靠的电源供应至关重要.
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
- 人工智能的人工智能
背景情况:
- 宽温度电解质 (WTE) 在不同的热条件下对稳定的离子电池 (LIB) 性能至关重要.
- 当前的电解质通常专门用于低温或高温,由于不同的性能要求,使WTE设计复杂化.
研究的目的:
- 开发人工智能辅助的工作流程,用于设计具有广泛操作温度范围的WTE.
- 确定新的电解质组件和配方,以提高电池的耐用性.
主要方法:
- 一个人工智能驱动的工作流涉及逐步参数化和计算被用于WTE设计.
- 线性单烯被研究为潜在的溶剂,因为它们的液体范围很广,离子溶解性能很弱.
- 使用可解释的AI模块来分析结构属性关系,识别化物和之间的相似性.
主要成果:
- 线性单烯被确定为WTE的有效溶剂.
- 确定3-甲基烯 (MPN) 是一个关键的溶剂,可以使电池在-60°C至120°C的温度下运行.
- 一个具有开发WTE的LiCoO2/Li细胞在100°C的50个循环后显示出72.3%的容量保留.
结论:
- 人工智能辅助的工作流成功设计了一种用于广泛温度LIB应用的新型WTE.
- 基于MPN的电解质在极端温度下增强电池稳定性和可靠性方面显示出显著的前景.
- 这种方法促进了先进的电解质的合理设计,以满足苛刻的储能应用.
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