Advancing battery research through large language models: A review

作者：Jianguo Chen, Yu Wang, Dongxu Guo, Zhiyong Liu, Yi Wang, Suran Li, Wenyuan Xu, Linglong Qian, Yifan Shen, Tao Sun, Xuebing Han, Minggao Ouyang, Yuejiu Zheng · 发表于：The Innovation · 年份：2025 · DOI：10.1016/j.xinn.2025.101091 · 被引用次数：5 · 研究领域：Topic Modeling、Speech Recognition and Synthesis

Rechargeable batteries are pivotal for achieving carbon neutrality and enabling the renewable energy transition. Their advancement requires innovations at micro (materials), device (manufacturing), and system (control and optimization) levels. However, traditional trial-and-error approaches are inadequate for modern scientific demands. As a transformative artificial intelligence (AI) technology, large language models (LLMs) deliver powerful semantic understanding and reasoning capabilities, driving a paradigm shift in battery research to address multilevel innovation needs. Nevertheless, this field still faces dual challenges: ambiguous technical roadmaps and fragmented progress in stage-specific achievements. This review systematically consolidates recent advances in applying LLMs to battery research, distilling core findings across four critical domains: knowledge integration, materials discovery, manufacturing processes, and system management. To address key bottlenecks-including limited model interpretability, inadequate alignment with electrochemical mechanisms, and real-world data adaptation challenges-we propose structured frameworks for deep integration of battery research and LLMs, alongside defined future technical pathways. These frameworks bridge fundamental battery science with AI-driven innovation paradigms to facilitate groundbreaking advances in next-generation battery technologies.