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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
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spelling v2 71692 2026-04-01 Accelerating discovery of MOFs for hydrogen storage via machine learning in energy related applications db028d01b9d62d39518f147f6bb08fa5 0000-0001-5566-171X Mary Larimi Mary Larimi true false 2026-04-01 EAAS 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. Journal Article Scientific Reports 0 Springer Science and Business Media LLC 2045-2322 Hydrogen Storage, Metal-Organic-Framework (MOFs), Artificial Neural Networks (ANNs), Grand Canonical Monte Carlo (GCMC), Temperature-Pressure Swing Conditions 18 3 2026 2026-03-18 10.1038/s41598-026-44340-8 COLLEGE NANME Engineering and Applied Sciences School COLLEGE CODE EAAS Swansea University 2026-04-01T10:16:17.7222843 2026-04-01T10:10:59.7166178 Faculty of Science and Engineering School of Engineering and Applied Sciences - Chemical Engineering Saeid Khairandesh 1 Marzieh Lotfi 2 Mary Larimi 0000-0001-5566-171X 3 Ali Akbar Asgharinezhad 4 Cyrus Ghotbi 5
title Accelerating discovery of MOFs for hydrogen storage via machine learning in energy related applications
spellingShingle Accelerating discovery of MOFs for hydrogen storage via machine learning in energy related applications
Mary Larimi
title_short Accelerating discovery of MOFs for hydrogen storage via machine learning in energy related applications
title_full Accelerating discovery of MOFs for hydrogen storage via machine learning in energy related applications
title_fullStr Accelerating discovery of MOFs for hydrogen storage via machine learning in energy related applications
title_full_unstemmed Accelerating discovery of MOFs for hydrogen storage via machine learning in energy related applications
title_sort Accelerating discovery of MOFs for hydrogen storage via machine learning in energy related applications
author_id_str_mv db028d01b9d62d39518f147f6bb08fa5
author_id_fullname_str_mv db028d01b9d62d39518f147f6bb08fa5_***_Mary Larimi
author Mary Larimi
author2 Saeid Khairandesh
Marzieh Lotfi
Mary Larimi
Ali Akbar Asgharinezhad
Cyrus Ghotbi
format Journal article
container_title Scientific Reports
container_volume 0
publishDate 2026
institution Swansea University
issn 2045-2322
doi_str_mv 10.1038/s41598-026-44340-8
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Chemical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Chemical Engineering
document_store_str 0
active_str 0
description 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.
published_date 2026-03-18T10:16:19Z
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score 11.4452305

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