Artificial intelligence-driven discovery and design of solid-state hydrogen storage materials

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.