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Large language models for batteries
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AI
DOI：10.1016/j.joule.2025.102037.png)
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Abstract
En 中文
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.
Keywords:
lithium-ion batteries
sodium-ion batteries
solid-state batteries
large language models
materials discovery