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Artificial Intelligence Empowers Solid-State Batteries for Material Screening and Performance Evaluation.

Sheng Wang1,2, Jincheng Liu3, Xiaopan Song4

  • 1School of Future Science and Engineering, Soochow University, Suzhou, 215222, People's Republic of China. shengwang@suda.edu.cn.

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|June 6, 2025
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Summary
This summary is machine-generated.

Machine learning (ML) accelerates solid-state battery development by efficiently screening materials and predicting performance. This review explores ML applications for discovering new battery components and optimizing battery management systems.

Keywords:
Artificial intelligenceDeep learningMaterial screeningPerformance evaluationSolid-state batteries

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Area of Science:

  • Materials Science
  • Electrochemistry
  • Computer Science

Background:

  • Solid-state batteries offer superior energy density and safety compared to conventional batteries.
  • Complex chemical environments and performance prediction challenges hinder solid-state battery industrialization.
  • Artificial intelligence (AI) and machine learning (ML) can significantly accelerate development.

Purpose of the Study:

  • To review the application of ML algorithms in discovering novel materials for solid-state batteries (cathodes, anodes, electrolytes).
  • To discuss the use of ML for predicting key performance indicators in solid-state battery management systems.
  • To identify current challenges and propose future research directions in ML for solid-state batteries.

Main Methods:

  • Systematic review of recent literature on ML applications in solid-state battery research.
  • Analysis of ML techniques for material database mining and property prediction.
  • Examination of ML models for state of charge, state of health, and remaining useful life estimation.

Main Results:

  • ML algorithms effectively accelerate the discovery of high-performance cathode, anode, and electrolyte materials.
  • ML enables accurate prediction of critical battery performance indicators, aiding battery management.
  • Identified challenges include data quality and code portability, with proposed solutions.

Conclusions:

  • ML is a powerful tool for advancing solid-state battery technology and facilitating industrialization.
  • Addressing data quality and code standardization is crucial for broader ML adoption.
  • Future research should focus on developing robust and portable ML solutions for solid-state batteries.