通过分子描述器框架,通过机器学习辅助对水性离子电池的电解质添加剂进行选
1Key Laboratory of Advanced Structural Materials, Ministry of Education and School of Materials Science and Engineering, Changchun University of Technology, Changchun, China.
Angewandte Chemie (International ed. in English)
|March 12, 2026
概括
机器学习加速了对稳定的水性离子电池的添加剂的发现. 这种方法识别了关键的分子描述符来预测性能,使得有效的选和提高电池寿命.
科学领域:
- 电化学 电化学 电化学
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
背景情况:
- 增材工程对于提高水性离子电池中的阳极稳定性至关重要.
- 添加性查的传统试错方法是低效的,阻碍了商业化.
研究的目的:
- 开发一个描述器框架驱动的战略,整合机器学习,以快速准确地选电解质添加剂.
- 确定影响阳极性能的关键描述因素,并阐明底层机制.
主要方法:
- 利用机器学习来识别分子大小/体积和最大静电电位 (ESPmax) 作为累积容量 (CC) 的关键描述符.
- 通过模拟和实验双重验证验证的结果.
- 采用了由开发的机器学习模型指导的描述符拟合和选方法.
主要成果:
- 通过使用已识别的描述符,实现了0.8707的高皮尔森相关系数,用于log(CC) 预测.
- 阐明了阳极上添加剂吸附的机制,调节界面水分子并抑制进化.
- 确定L-酸盐 (PL-As) 作为一种高性能添加剂.
结论:
- 机器学习驱动的描述器框架可以有效和准确地选水性离子电池的添加剂.
- 鉴定出的添加剂PL-As显著提高了阳极的稳定性,在5000个循环中实现了99.86%的库伦比效率.
- 这种方法为开发高稳定性水性电解质添加剂和推进电池技术建立了新范式.
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