Transmission, Distribution & Storage

The development of efficient and sustainable hydrogen storage materials is a key challenge for realizing hydrogen as a clean and flexible energy carrier. Among various options metal hydrides offer high volumetric storage density and operational safety yet their application is limited by thermodynamic kinetic and compositional constraints. In this work we investigate the potential of machine learning (ML) to predict key thermodynamic properties—equilibrium plateau pressure enthalpy and entropy of hydride formation—based solely on alloy composition using Magpie-generated descriptors. We significantly expand an existing experimental dataset from ~400 to 806 entries and assess the impact of dataset size and data augmentation using the PADRE algorithm on model performance. Models including Support Vector Machines and Gradient Boosted Random Forests were trained and optimized via grid search and cross-validation. Results show a marked improvement in predictive accuracy with increased dataset size while data augmentation benefits are limited to smaller datasets and do not improve accuracy in underrepresented pressure regimes. Furthermore clustering and cross-validation analyses highlight the limited generalizability of models across different material classes though high accuracy is achieved when training and testing within a single hydride family (e.g. AB2). The study demonstrates the viability and limitations of ML for accelerating hydride discovery emphasizing the importance of dataset diversity and representation for robust property prediction.

This study investigates hydrogen storage enhancement through adsorption in porous materials by coupling the Dubinin–Astakhov (D-A) adsorption model with H2 conservation equations (mass momentum and energy). The resulting system of partial differential equations (PDEs) was solved numerically using the finite element method (FEM). Experimental work using activated carbon as an adsorbent was carried out to validate the model. The comparison showed good agreement in terms of temperature distribution average pressure of the system and the amount of adsorbed hydrogen (H2). Further simulations with different adsorbents indicated that compact metal–organic framework 5 (MOF-5) is the most effective material in terms of H2 adsorption. Additionally the pair (273 K 800 s) remains the optimal combination of injection temperature and time. The findings underscore the prospective advantages of optimized MOF-5-based systems for enhanced hydrogen storage. These systems offer increased capacity and safety compared to traditional adsorbents. Subsequent research should investigate multi-objective optimization of material properties and system geometry along with evaluating dynamic cycling performance in practical operating conditions. Additionally experimental validation on MOF-5-based storage prototypes would further reinforce the model’s predictive capabilities for industrial applications.