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Coevolution of large language models with physical models boosts advanced battery research

Year: 2025

Type: article

Abstract: With the advent of large language models (LLMs), deep artificial intelligence methods have begun to approach the field of scientific research. Nevertheless, the pre-trained LLMs face problems such as information fabrication and the inability to handle simulations in specific research fields, which restricts the further integration of artificial intelligence with professional scientific research. Here, we present a pioneering framework that harnesses the textual comprehension of LLMs alongside the physical models, thereby advancing the field of battery research. Our coevolution blueprint not only refines the LLMs' ability to conduct domain-specific simulations but also generates data, steering AI toward broader intelligence. The integration of LLMs and physical models provides a powerful tool for researchers, streamlining the transition from data analysis to experimental validation, and inspires more researchers to adopt this paradigm as a prototype to promote the integration between data, models, and information, innovating the research patterns in related fields.

Source: Cell Reports Physical Science

DOI: 10.1016/j.xcrp.2025.102553