Large language models for batteries

Abstract

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.