Artificial intelligence-driven discovery and design of solid-state hydrogen storage materialsOA
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.
Yuyi Chu;Yanxin Li;Yanxi Lyu;Di Zhang;Hao Li;Weijie Yang;Jianqiu Li;Liang Zhang
Center for Combustion Energy,Tsinghua University,Beijing,100084,China School of Vehicle and Mobility,State Key Laboratory of Intelligent Green Vehicle and Mobility,Tsinghua University,Beijing,100084,ChinaDepartment of Power Engineering,School of Energy,Power and Mechanical Engineering,North China Electric Power University,Baoding,071003,ChinaCenter for Combustion Energy,Tsinghua University,Beijing,100084,China School of Vehicle and Mobility,State Key Laboratory of Intelligent Green Vehicle and Mobility,Tsinghua University,Beijing,100084,China Beijing Huairou Laboratory,Beijing,ChinaAdvanced Institute for Materials Research(WPI-AIMR),Tohoku University,Sendai,980-8577,JapanAdvanced Institute for Materials Research(WPI-AIMR),Tohoku University,Sendai,980-8577,JapanDepartment of Power Engineering,School of Energy,Power and Mechanical Engineering,North China Electric Power University,Baoding,071003,ChinaSchool of Vehicle and Mobility,State Key Laboratory of Intelligent Green Vehicle and Mobility,Tsinghua University,Beijing,100084,China Beijing Huairou Laboratory,Beijing,ChinaCenter for Combustion Energy,Tsinghua University,Beijing,100084,China School of Vehicle and Mobility,State Key Laboratory of Intelligent Green Vehicle and Mobility,Tsinghua University,Beijing,100084,China Beijing Huairou Laboratory,Beijing,China
信息技术与安全科学
Solid-State Hydrogen StorageDatabasesFeature EngineeringMachine LearningAI Platforms
《Green Energy & Environment》 2026 (4)
P.940-967,28
supported by the National Natural Science Foundation of China(22521202,22373055,52250710681)the Program of Beijing Huairou Laboratory(ZD2022006A)the Tsinghua University Dushi Research Programthe Natural Science Foundation of Hebei(Grant No.E2023502006)the Fundamental Research Fund for the Central Universities(2025MS131)the Fundamental Research Funds for the Central Universities(Grant No.2025JC008)the financial support from JSPS KAKENHI(grant nos.JP25K01737,JP25K17991,JP24K23068,and JP25H01508)the Hirose Foundation.
评论