Artificial Intelligence-Driven Innovations in Hydrogen Storage Technology

Abstract

In the global transition towards sustainable energy sources, hydrogen energy has emerged as an indispensable pillar in reshaping the energy landscape, owing to its environmental sustainability, zero emissions, and high efficiency. Nevertheless, the large-scale deployment of hydrogen energy is confronted with substantial technical barriers in storage and transportation. Although contemporary research has shifted focus to the development of highly efficient hydrogen storage materials, conventional material design concepts remain predominantly empirical, typically relying on trial-and-error methodologies. Importantly, the widespread application of artificial intelligence technologies in accelerating materials discovery and optimization has attracted considerable attention. This review provides a comprehensive overview of the latest advancements in hydrogen storage technologies, with an emphasis on the synergistic application of high-throughput screening and machine learning in solid-state hydrogen storage materials. These approaches demonstrate exceptional potential in accurately predicting hydrogen storage properties, optimizing material performance, and accelerating the development of innovative hydrogen storage materials. Specifically, we discuss in detail the essential role of artificial intelligence in developing hydrogen storage materials such as metal hydrides, alloys, carbon materials, metal–organic frameworks, and zeolites. Moreover, underground hydrogen storage is further explored as a scalable renewable energy storage solution, particularly in terms of optimizing storage parameters and performance prediction. By systematically analyzing the limitations of existing hydrogen storage approaches and the transformative potential of artificial intelligence-driven methods, this review offers insights into the discovery and optimization of high-performance hydrogen storage materials, contributing to sustainable global energy development and technological innovation.

Keywords

environmental protection / high-throughput screening / hydrogen energy / hydrogen storage / machine learning

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