对于固态电池的可解释机器学习
Xinyu Ye1, Yaxin Cheng1,2, Xuexia Lan1
1Institute of Technology for Carbon Neutrality, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
ACS nano
|February 2, 2026
概括
本研究介绍了在固态电池 (SSB) 研究中可解释机器学习 (ML) 的框架. 它的目的是增强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
- 计算科学 计算科学
背景情况:
- 固态电池 (SSB) 为下一代储能提供高能量密度和安全性.
- 机器学习 (ML) 加快了电池材料的发现和性能预测.
- 当前ML模型的"黑盒子"性质限制了它们在电池研究中的解释性和可信性.
研究的目的:
- 为推进固态电池研究提出一个结构化,可解释的ML框架.
- 为了解决当前ML模型在理解电池机制方面的局限性.
- 为了促进从"黑子"预测到SSB的机制驱动设计的过渡.
主要方法:
- 为五个关键SSB组件开发可解释的ML方法:固体电解质设计,材料表征,电极/电解质接口优化,电池寿命预测和树抑制.
- 确定每个组件的特定要求并推适合的ML方法.
- 总结当前的挑战并提出解决方案,包括开源工具链.
主要成果:
- 一个全面的框架,将可解释的ML集成到整个SSB研究管道中.
- 针对SSB开发的关键方面量身定制的特定ML策略.
- 一个路线图,以提高ML在电池科学中的科学可信度和实际应用.
结论:
- 可解释的ML对于加速高性能固态电池的开发至关重要.
- 使用可解释的ML的结构化,机制驱动的方法将克服当前的研究瓶.
- 拟议的框架和工具链将促进储能技术的创新.
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