Evidence map›Paper›PMID 41658497›Full record

ReviewInnovation (Cambridge (Mass.))2026

Advancing battery research through large language models: A review.

Jianguo Chen, Yu Wang, Dongxu Guo, Zhiyong Liu, Yiduo Wang, Suran Li, Wendong Xu, Linglong Qian, Yifan Shen, Tao Sun and 3 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Innovation (Cambridge (Mass.)), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Jianguo ChenSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Yu WangState Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing 100084, China.
Dongxu GuoSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Zhiyong LiuSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Yiduo WangSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Suran LiSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Wendong XuSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Linglong QianSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Yifan ShenSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Tao SunSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Xuebing HanState Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing 100084, China.
Minggao OuyangState Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing 100084, China.
Yuejiu ZhengSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rechargeable batteries are pivotal for achieving carbon neutrality and enabling the renewable energy transition. Their advancement requires innovations 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. Nevertheless, this field still faces dual challenges: ambiguous technical roadmaps and fragmented progress in stage-specific achievements. This review systematically 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 interpretability, 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.

Indexed as

battery intelligent manufacturingbattery knowledge integrationbattery materials developmentbattery system managementlarge language models

Identifiers

PMID41658497
PMCPMC12881755

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.