Computational intelligence approaches for hydrogen storage material design

Abstract Hydrogen is increasingly recognized as a clean energy carrier in the transition toward low‐carbon and sustainable energy systems. However, the development of safe, efficient, and cost‐effective hydrogen storage technologies remains a major barrier to its large‐scale deployment. This paper presents a review of how advanced computational intelligence encompassing machine learning (ML), deep learning (DL), high‐throughput screening, and multi‐scale modelling is transforming the discovery, design, and optimization of hydrogen storage materials. The integration of graph neural networks (GNNs), inverse material design, and Bayesian optimization is enabling accelerated screening and performance prediction across diverse material classes such as metal hydrides, complex hydrides, porous frameworks, and carbon nanostructures. Materials informatics platforms and autonomous discovery pipelines, including self‐driving laboratories and active learning workflows, are further streamlining the experimental validation of computationally designed materials. In addition, the application of quantum ML and multi‐fidelity modelling is improving accuracy while reducing simulation costs. Through a series of case study reviews, this paper demonstrates the critical role of these tools in enhancing thermodynamic tuning, adsorption capacity, and kinetic response in next‐generation hydrogen storage systems. This review study highlight that computational intelligence is central to the hydrogen materials innovation cycle.