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Advancing battery research through large language models: A review.

Jianguo Chen1, Yu Wang2, Dongxu Guo1,2

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Large language models (LLMs) are revolutionizing rechargeable battery research for carbon neutrality. This review details LLM applications in materials, manufacturing, and systems, proposing frameworks for AI-driven battery innovation.

Keywords:
battery intelligent manufacturingbattery knowledge integrationbattery materials developmentbattery system managementlarge language models

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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.