人工智能赋能固态电池用于材料选和性能评估
Sheng Wang1,2, Jincheng Liu3, Xiaopan Song4
1School of Future Science and Engineering, Soochow University, Suzhou, 215222, People's Republic of China. shengwang@suda.edu.cn.
Nano-micro letters
|June 6, 2025
概括
机器学习 (ML) 通过有效选材料和预测性能来加速固态电池的开发. 本综述探讨了ML的应用,用于发现新的电池组件和优化电池管理系统.
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
- 计算机科学 计算机科学
背景情况:
- 与传统电池相比,固态电池提供更高的能量密度和安全性.
- 复杂的化学环境和性能预测挑战阻碍了固态电池的工业化.
- 人工智能 (AI) 和机器学习 (ML) 可以显著加速开发.
研究的目的:
- 审查ML算法在发现固态电池新材料 (阴极,阳极,电解质) 的应用.
- 讨论使用ML来预测固态电池管理系统中的关键性能指标.
- 确定当前的挑战,并提出未来的研究方向在ML固态电池.
主要方法:
- 在固态电池研究中对ML应用的最新文献进行系统审查.
- 对材料数据库挖掘和财产预测的ML技术的分析.
- 检查ML模型的充电状态,健康状况和剩余使用寿命估计.
主要成果:
- ML算法有效地加速了高性能阴极,阳极和电解质材料的发现.
- ML可以准确预测关键电池性能指标,帮助电池管理.
- 确定的挑战包括数据质量和代码可移植性,并提出解决方案.
结论:
- 机器学习是推进固态电池技术和促进工业化的强大工具.
- 解决数据质量和代码标准化对于更广泛的ML采用至关重要.
- 未来的研究应该专注于为固态电池开发强大的和便携式ML解决方案.
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