通过大型语言模型推进电池研究:一篇综述
Jianguo Chen1, Yu Wang2, Dongxu Guo1,2
1School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Innovation (Cambridge (Mass.))
|February 9, 2026
概括
大型语言模型 (LLM) 正在彻底改变可充电电池研究的碳中和性. 本综述详细介绍了材料,制造和系统中的LLM应用,并提出了人工智能驱动的电池创新的框架.
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
- 人工智能的人工智能
背景情况:
- 可充电电池对于可再生能源和碳中和性至关重要.
- 传统的电池开发方法不足以满足当前的科学需求.
- 进步需要在材料,制造和系统控制方面进行创新.
研究的目的:
- 系统地审查大型语言模型 (LLM) 在可充电电池研究中的应用.
- 巩固在知识整合,材料发现,制造和系统管理中对LLM的发现.
- 提出将LLM整合到电池科学中的框架,以克服当前的挑战.
主要方法:
- 系统的文献审查,对电池研究LLM应用的最新进展.
- 分析四个关键领域的LLM贡献:知识整合,材料发现,制造过程和系统管理.
- 识别瓶,例如有限的解释性和数据适应挑战.
主要成果:
- 课程提供了强大的语义理解和推理,用于多层次的电池创新.
- 最近的进展表明,LLM在加速各种领域的电池研究方面具有潜力.
- 关键的挑战包括模型的可解释性,与电化学原理的调整以及现实世界的数据集成.
结论:
- 将LLM整合到电池研究中,为下一代技术提供了一个范式转变.
- 拟议的框架旨在将基础电池科学与人工智能驱动的创新联系起来.
- 解决目前的瓶对于释放LLM在电池开发中的全部潜力至关重要.
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