Advancing battery research through large language models: A review

Area of Science:

• Materials Science
• Energy Storage
• Artificial Intelligence

Background:

• Rechargeable batteries are crucial for renewable energy and carbon neutrality.
• Traditional battery development methods are insufficient for current scientific demands.
• Advancements require innovation across materials, manufacturing, and system control.

Purpose of the Study:

• To systematically review the application of large language models (LLMs) in rechargeable battery research.
• To consolidate findings on LLMs in knowledge integration, materials discovery, manufacturing, and system management.
• To propose frameworks for integrating LLMs into battery science to overcome current challenges.

Main Methods:

• Systematic literature review of recent advances in LLM applications for battery research.
• Analysis of LLM contributions across four key domains: knowledge integration, materials discovery, manufacturing processes, and system management.
• Identification of bottlenecks such as limited interpretability and data adaptation challenges.

Main Results:

• LLMs offer powerful semantic understanding and reasoning for multilevel battery innovation.
• Recent advances show LLM potential in accelerating battery research across various domains.
• Key challenges include model interpretability, alignment with electrochemical principles, and real-world data integration.

Conclusions:

• Integrating LLMs into battery research offers a paradigm shift for next-generation technologies.
• Proposed frameworks aim to bridge fundamental battery science with AI-driven innovation.
• Addressing current bottlenecks is essential for unlocking the full potential of LLMs in battery development.