Systematic review demonstrates structure–property relationships of functionalized TiO2 catalysts for MgH2 solid-state hydrogen storage, highlighting pathways for machine learning–guided design.
TiO 2 has emerged as a pivotal catalyst for enhancing MgH 2 , a high‐capacity solid‐state hydrogen storage material, owing to its structural versatility. Extensive studies have engineered functionalized TiO 2 variants through modifications of particle size, morphology, crystalline phase, electronic structure, and heterojunction configurations, thereby enhancing its catalytic properties. However, the vast scope of modulation strategies introduces combinatorial complexities, making the discovery of optimally engineered TiO 2 an ongoing challenge. Here, we systematically review the structure–property–performance relationships of functionalized TiO 2 for MgH 2 catalysis, consolidating experimental trends across size, phase, doping, and composite regulation strategies. We highlight how the accumulated experimental and computational data now provide a timely foundation for data‐driven and machine‐learning‐assisted catalyst design. By identifying key descriptors from existing modulation approaches, we aim to guide dataset construction and model refinement for efficient catalyst design, ultimately guiding the rational design of high‐performance TiO 2 ‐based catalysts.
No takes yet. Share an insight, caveat, or question.
Chu et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: