走向人工智能生态系统,用于固态电池中的电解质和接口工程
Zhilong Wang1,2,3, Wolfgang G Zeier4,5, Fengqi You1,2,3
1Cornell University AI for Science Institute, Cornell University, Ithaca, NY 14853, USA.
Science advances
|November 26, 2025
概括
人工智能 (AI) 正通过优化电解质和接口设计来加速固态电池 (SSB) 的创新. 这篇评论强调了人工智能.
科学领域:
- 材料科学 材料科学 材料科学
- 储能 储能 储能 储能 储能 储能
- 计算化学计算化学
背景情况:
- 固态电池 (SSB) 为可持续的能源存储提供了卓越的安全性和寿命.
- 开发稳定的固体电解质和接口是SSB进步的关键瓶.
研究的目的:
- 审查人工智能 (AI) 在工程固体电解质和SSB接口中的应用.
- 探索AI的潜力,以克服SSB开发中的关键挑战,包括稳定性,导电性和接口电阻.
主要方法:
- 整合人工智能驱动的材料发现管道.
- 机器学习力场和生成模型用于材料设计的应用.
- 分析多尺度和多模式建模策略.
主要成果:
- 人工智能在选材料和预测SSB组件特性方面显示出巨大潜力.
- 目前的AI方法解决了固体电解质中的稳定性,导电性和接口电阻.
- 生成型模型显示出设计新型电解质和接口材料的前景.
结论:
- 人工智能是加速SSB创新和克服设计挑战的变革性工具.
- 智能SSB开发生态系统的路线图涉及多尺度建模和基于物理的AI.
- 跨学科的合作对于通过人工智能推进可持续电池技术至关重要.
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