Advances in Hydrogen Storage Materials: Mechanistic Insights and AI Applications

Abstract

Hydrogen energy is a clean and efficient form of energy, and it is getting more and more attention for its wide use in transportation, energy systems, and industry. But because hydrogen has low density and spreads easily, storing it safely, efficiently, and at low cost is still a big challenge. Many materials like metal–organic frameworks (MOFs), metal hydrides, and carbon-based nanomaterials have been studied for storing hydrogen. However, most of them still have problems, such as low storage ability, needing strict conditions, and not working well after repeated use. These issues make them hard to use in real life. In recent years, artificial intelligence (AI) has brought big changes to hydrogen storage research. Machine learning and deep learning help predict material performance, understand how things work, and design new materials. Together with lab experiments, AI forms a full cycle of “design, test, and improve,” which speeds up the development of better materials. This paper presents a comprehensive review of mainstream hydrogen storage mechanisms and materials, evaluates their practical limitations, and highlights how AI techniques are being applied to address these challenges. The study aims to support the rational design of next-generation hydrogen storage systems and promote deeper integration between AI and materials science to advance the scalable adoption of hydrogen energy.