Rechargeable batteries are pivotal for achieving carbon neutrality and enabling the renewable energy transition.Their advancement requires inno-vations at micro(materials),device(manufacturing),and system(control and ...Rechargeable batteries are pivotal for achieving carbon neutrality and enabling the renewable energy transition.Their advancement requires inno-vations at micro(materials),device(manufacturing),and system(control and optimization)levels.However,traditional trial-and-error approaches are inadequate for modern scientific demands.As a transformative artificial intelligence(AI)technology,large language models(LLMs)deliver powerful semantic understanding and reasoning capabilities,driving a paradigm shift in battery research to address multilevel innovation needs.Neverthe-less,this field still faces dual challenges:ambiguous technical roadmaps and fragmented progress in stage-specific achievements.This review sys-tematically consolidates recent advances in applying LLMs to battery research,distilling core findings across four critical domains:knowledge integration,materials discovery,manufacturing processes,and system management.To address key bottlenecks—including limited model inter-pretability,inadequate alignment with electrochemical mechanisms,and real-world data adaptation challenges—we propose structured frameworks for deep integration of battery research and LLMs,alongside defined future technical pathways.These frameworks bridge fundamental battery science with AI-driven innovation paradigms to facilitate groundbreaking advances in next-generation battery technologies.展开更多
基金supported by the National Natural Science Foundation of China under grant nos.52277222,52406256,52177217the Shuimu Tsinghua Scholar Program(grant no.2022SM146)an Artificial Intelligence for Research Paradigm Reform Enabling Discipline Leapfrog Program Project Funding Grant.
文摘Rechargeable batteries are pivotal for achieving carbon neutrality and enabling the renewable energy transition.Their advancement requires inno-vations at micro(materials),device(manufacturing),and system(control and optimization)levels.However,traditional trial-and-error approaches are inadequate for modern scientific demands.As a transformative artificial intelligence(AI)technology,large language models(LLMs)deliver powerful semantic understanding and reasoning capabilities,driving a paradigm shift in battery research to address multilevel innovation needs.Neverthe-less,this field still faces dual challenges:ambiguous technical roadmaps and fragmented progress in stage-specific achievements.This review sys-tematically consolidates recent advances in applying LLMs to battery research,distilling core findings across four critical domains:knowledge integration,materials discovery,manufacturing processes,and system management.To address key bottlenecks—including limited model inter-pretability,inadequate alignment with electrochemical mechanisms,and real-world data adaptation challenges—we propose structured frameworks for deep integration of battery research and LLMs,alongside defined future technical pathways.These frameworks bridge fundamental battery science with AI-driven innovation paradigms to facilitate groundbreaking advances in next-generation battery technologies.
