Machine learning-guided design of engineered metal-organic frameworks for sustainable hydrogen storage and production

Hydrogen is a promising clean energy carrier, but efficient materials for hydrogen production and storage remain challenging to develop. Metal-organic frameworks (MOFs) offer unique advantages, including high surface area, tunable pores, and versatile chemistry; however, their structural complexity hinders conventional discovery approaches. This review presents a critical analysis of machine learning (ML)-guided engineering of MOFs for sustainable hydrogen technologies, covering doping, defect engineering, functionalization, composites, heterostructures, and single-atom catalysts. Unlike previous reviews, this work emphasizes the integration of ML with rational MOF design to establish structure-performance relationships. Ensemble learning, deep learning, and graph-based models show high predictive capability for hydrogen adsorption, catalytic activity, and material properties, while explainable ML reveals key descriptors. Defect engineering and doping show strong practical potential, whereas single-atom MOFs require improved stability and scalable synthesis. Emerging strategies, including explainable AI, autonomous laboratories, digital twins, and quantum ML, are discussed for future MOF discovery.