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AI-driven exploration and design of inorganic battery materials

Author

Listed:
  • Wang, Xuesong
  • He, Nan
  • Zhou, Zidong
  • Chen, Jing
  • Mateen, Abdul
  • Shao, Yinfei
  • Chen, Xiang
  • Liu, Chengbin
  • Mujear, Altaf
  • Zhang, Jianwei
  • Liu, Feng
  • Bao, Zhihao

Abstract

The increasing demand for high-performance batteries in electric vehicles, portable electronics, and renewable energy storage has spurred extensive research into novel inorganic battery materials. This review comprehensively examines the role of artificial intelligence (AI) in accelerating the discovery, design, and optimization of inorganic battery components, including cathodes, anodes, and solid-state electrolytes. We discuss various AI methodologies-such as machine learning, deep learning, transfer learning, generative models, and large language models-that integrate experimental and computational data to predict material properties and uncover structure-property relationships. Special emphasis is placed on data acquisition and standardization, model interpretability, and the challenges associated with multi-scale integration. By highlighting recent case studies and high-throughput screening strategies, the review elucidates how AI-driven approaches can overcome the limitations of traditional trial-and-error methods and high computational costs. Finally, we outline future research directions, emphasizing the potential of emerging AI techniques to further enhance material performance and accelerate the industrialization of next-generation battery technologies.

Suggested Citation

  • Wang, Xuesong & He, Nan & Zhou, Zidong & Chen, Jing & Mateen, Abdul & Shao, Yinfei & Chen, Xiang & Liu, Chengbin & Mujear, Altaf & Zhang, Jianwei & Liu, Feng & Bao, Zhihao, 2026. "AI-driven exploration and design of inorganic battery materials," Renewable and Sustainable Energy Reviews, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:rensus:v:229:y:2026:i:c:s1364032125013061
    DOI: 10.1016/j.rser.2025.116633
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