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Yuyi Chu, Yanxin Li, Yanxi Lyu, Di Zhang, Hao Li, Weijie Yang, Jianqiu Li, Liang Zhang. Artificial Intelligence‐Driven Discovery and Design of Solid-State Hydrogen Storage Materials. Green Energy&Environment. doi: 10.1016/j.gee.2026.01.007
Citation: Yuyi Chu, Yanxin Li, Yanxi Lyu, Di Zhang, Hao Li, Weijie Yang, Jianqiu Li, Liang Zhang. Artificial Intelligence‐Driven Discovery and Design of Solid-State Hydrogen Storage Materials. Green Energy&Environment. doi: 10.1016/j.gee.2026.01.007
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Artificial Intelligence‐Driven Discovery and Design of Solid-State Hydrogen Storage Materials

doi: 10.1016/j.gee.2026.01.007

  • a. Center for Combustion Energy, Tsinghua University, Beijing, 100084, China;

  • b. School of Vehicle and Mobility, State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing, 100084, China;

  • c. Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, 071003, China;

  • d. Advanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai, 980-8577, Japan;

  • e. Beijing Huairou Laboratory, Beijing, China

Funds:

L.Z. acknowledges the financially supported by the National Natural Science Foundation of China (22521202, 22373055, 52250710681), the Program of Beijing Huairou Laboratory (ZD2022006A), the Tsinghua University Dushi Research Program. W.Y. acknowledges financial support for this work from the Natural Science Foundation of Hebei (Grant No. E2023502006), the Fundamental Research Fund for the Central Universities (2025MS131) and the Fundamental Research Funds for the Central Universities (Grant No. 2025JC008). D.Z. and H.L. are grateful for the financial support from JSPS KAKENHI (grant nos. JP25K01737, JP25K17991, JP24K23068, and JP25H01508), the Hirose Foundation.

  • Received date 11 September 2025, Accepted date 19 January 2026, RevRecd date 02 December 2025, Available online 28 February 2026
    Abstract
  • Artificial intelligence (AI) is reshaping the discovery and optimization of solid-state hydrogen storage materials, a cornerstone of a scalable hydrogen economy. However, interdependent trade-offs among capacity, operating conditions, and cycling stability still limit progress. This Review surveys AI-assisted advances in metallic hydrogen storage through the co-design of features and models. We consolidate descriptor sets that fuse intrinsic crystal, electronic-structure, and thermodynamic properties with extrinsic experimental conditions. We also systematically summarize machine-learning approaches for performance prediction, physics-informed simulation, and materials and process optimization. We additionally describe AI-driven platforms that integrate curated datasets, forward–inverse modeling, workflow orchestration, and user-facing tools for high-throughput screening and synthesis-aware decision-making. Looking ahead, interpretability, cross-scale modeling, and large language model driven closed-loop discovery will accelerate the practical deployment of solid-state hydrogen storage.

     

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