2026-06-02 アルゴンヌ国立研究所（ANL）
◆研究者らは、材料特性や実験結果の大規模データをAIに学習させることで、有望な電極材料や電解質の候補を迅速に絞り込み、試行錯誤の回数を削減できると期待している。また、AIは電池内部で起こる複雑な化学反応や劣化機構の解析にも活用され、寿命や安全性の向上につながる知見の獲得を支援する。さらに、実験施設や放射光施設で得られる大量データをリアルタイムで処理し、研究者の意思決定を支援する仕組みの構築も進められている。
◆この取り組みは、電気自動車や再生可能エネルギー蓄電システム向けの高性能・低コスト電池の開発期間を短縮し、エネルギー転換を支える革新的技術基盤となることが期待されている。
＜関連情報＞
- https://www.anl.gov/article/turbocharging-battery-research-with-ai-an-ambitious-vision
- https://www.sciencedirect.com/science/article/abs/pii/S2542435125002181
バッテリーの大規模言語モデル Large language models for batteries
Wenhua Zuo, Huihuo Zheng, Tanjin He, Venkatram Vishwanath, Maria K.Y. Chan, Rick L. Stevens, Khalil Amine, Gui-Liang Xu
Joule Available online: 20 August 2025
DOI:https://doi.org/10.1016/j.joule.2025.102037
Graphical abstract
Context & scale
Electrochemical rechargeable batteries—especially lithium-ion, sodium-ion, and solid-state batteries—are crucial for meeting increasing global energy demands. However, their advancement faces challenges related to material discovery, performance optimization, and manufacturing scalability. Traditional experimental methods, while effective, are often time-consuming and resource-intensive, limiting the pace of innovation. Large language models (LLMs) are designed to generate humanlike text and solve complex problems by analyzing vast amounts of text and data. With continuous advancement in data, model architecture, and computation resources, the capability of LLMs has evolved significantly from basic applications of text mining and education to more specialized areas—including materials representation, data interpretation, hypothesis generation, and lab automation—enabling their increasing prominence in science domains. The rapid development of batteries requires the integration of diverse fields, including materials science, chemistry, and engineering.
LLMs hold the potential to revolutionize both the theoretical understanding and practical applications of battery technology, while the battery research community has yet to fully harness their powerful capabilities. In battery research, LLMs offer unparalleled opportunities to orchestrate specialized computational and experimental tools, synthesize vast multi-source electrochemical data, and generate design and optimization strategies for electrode and electrolyte materials. In device-level applications, they can also facilitate real-time diagnostics of battery behavior and adjustments of operating parameters for extended battery life and enhanced safety. This review outlines a systematic approach to deploying LLMs in battery research, including the pivotal roles that LLMs can play for batteries, criteria for model selection, and critical challenges faced by battery-specific models.
Summary
Large language models (LLMs) are advanced artificial intelligence systems capable of solving diverse tasks using language, reasoning, and external tools. Despite their growing deployment in academia and industry, their potential remains underexplored in battery research. This review presents a comprehensive overview of existing and emerging applications of LLMs in the battery field, addressing two critical questions: what can LLMs offer to support battery-related tasks, and how can more effective models be developed for this purpose? We begin by outlining the principles of LLMs and criteria for selecting appropriate models and tools for battery research and development. We then explore the roles of LLMs in text mining, data interpretation, and the development of intelligent battery systems. In parallel, we discuss technical challenges, such as data standardization and sharing, model evaluation, and tool integration. Finally, we propose future research directions with short-, medium-, and long-term goals and highlight more broad perspectives for connecting experts and cross-disciplinary collaborations.
