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Accelerating discovery of MOFs for hydrogen storage via machine learning in energy related applications

Saeid Khairandesh, Marzieh Lotfi, Mary Larimi Orcid Logo, Ali Akbar Asgharinezhad, Cyrus Ghotbi

Scientific Reports

Swansea University Author: Mary Larimi Orcid Logo

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DOI (Published version): 10.1038/s41598-026-44340-8

Abstract

Hydrogen is a promising clean energy carrier, but its low energy density necessitates advanced storage solutions. Metal-Organic Frameworks (MOFs) offer high tunability and porosity for efficient hydrogen adsorption. This work combines Grand Canonical Monte Carlo (GCMC) simulations with machine learn...

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Published in: Scientific Reports
ISSN: 2045-2322
Published: Springer Science and Business Media LLC 2026
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URI: https://cronfa.swan.ac.uk/Record/cronfa71692
Abstract: Hydrogen is a promising clean energy carrier, but its low energy density necessitates advanced storage solutions. Metal-Organic Frameworks (MOFs) offer high tunability and porosity for efficient hydrogen adsorption. This work combines Grand Canonical Monte Carlo (GCMC) simulations with machine learning, employing Feed-Forward (FNN) and Pattern Recognition (PRNN) neural networks optimized via Equilibrium Optimizer and Genetic Algorithm. The integrated approach predicts gravimetric and volumetric hydrogen storage capacities across 98,695 metal-organic frameworks under temperature-pressure swing conditions. Pore volume and void fraction emerged as dominant structural descriptors. The models identified 12 top-performing MOFs exceeding MOF-5 in both gravimetric (8.27 wt.%) and volumetric (51.94 g-H /L) capacities, demonstrating the power of ML-accelerated screening for next-generation hydrogen storage materials.
Keywords: Hydrogen Storage, Metal-Organic-Framework (MOFs), Artificial Neural Networks (ANNs), Grand Canonical Monte Carlo (GCMC), Temperature-Pressure Swing Conditions
College: Faculty of Science and Engineering

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