加快电池创新:人工智能驱动的分子发现.
Yu-Chen Gao1, Xiang Chen1,2,3, Yu-Hang Yuan1
1Beijing Key Laboratory of Complex Solid State Batteries & Tsinghua Center for Green Chemical Engineering Electrification, Department of Chemical Engineering, Tsinghua University, Beijing 100084, P.R. China. xiangchen@mail.tsinghua.edu.cn.
Chemical Society reviews
|September 22, 2025
概括
人工智能 (AI) 加快了先进电池的分子设计,提高了能量密度,寿命和安全性. 本综述探讨了人工智能战略,算法和可持续能源创新的应用.
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
- 计算化学的计算化学
背景情况:
- 全球能源转型需要先进的电池技术来应对气候变化.
- 分子工程对于改善电池性能指标,如能量密度,循环寿命和安全性至关重要.
研究的目的:
- 系统地审查人工智能 (AI) 在下一代电池的分子发现中的整合.
- 解决AI在电池开发中的变革潜力和可持续性挑战.
主要方法:
- 描述分子表示的多维策略,为AI创建机器可读的输入.
- 人工智能算法的系统总结,包括经典机器学习,深度学习和大型语言模型.
- 用人工智能来预测关键的电化学性质 (氧化还原潜力,粘度,介电常数) 的图示.
主要成果:
- 人工智能通过化学知识发现,高通量虚拟选和面向分子生成来促进分子设计.
- 人工智能集成可实现高吞吐量实验,以加速电池材料开发.
- 人工智能驱动的预测显著提高了对电化学性质的理解和优化.
结论:
- 人工智能对于加速下一代电池系统的分子设计至关重要.
- 整合分子数据库,算法,计算能力和自主实验平台是未来创新的关键.
- 人工智能预计将通过先进的电池开发推动可持续能源创新.
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