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

Saeid Khairandesh, Marzieh Lotfi, Afsanehsadat Larimi, Ali Akbar Asgharinezhad, Cyrus Ghotbi

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In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Saeid KhairandeshDepartment of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran.

Marzieh LotfiChemical Engineering Department, Jundi-Shapur University of Technology, Dezful, Iran. marzyeh.lotfi@gmail.com.

Afsanehsadat LarimiDepartment of Chemical Engineering, School of Engineering and Applied Sciences, Swansea University, Wales, UK. a.larimi@swansea.ac.uk.

Ali Akbar AsgharinezhadChemistry and Process Research Department, Niroo Research Institute, Tehran, Iran. aasgharinezhad@nri.ac.ir.

Cyrus GhotbiDepartment of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Indexed as

Artificial neural networks (ANNs)Grand canonical Monte Carlo (GCMC)Hydrogen storageMetal–organic-framework (MOFs)Temperature–pressure swing conditions

Identifiers

PMID41851282

DOI10.1038/s41598-026-44340-8

PMCPMC13136365

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.