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

doi:10.1038/s41598-026-44340-8

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

Sci Rep. 2026 Mar 18;16(1):14114. doi: 10.1038/s41598-026-44340-8.

Authors

Saeid Khairandesh¹, Marzieh Lotfi², Afsanehsadat Larimi³, Ali Akbar Asgharinezhad⁴, Cyrus Ghotbi¹

Affiliations

¹ Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran.
² Chemical Engineering Department, Jundi-Shapur University of Technology, Dezful, Iran. marzyeh.lotfi@gmail.com.
³ Department of Chemical Engineering, School of Engineering and Applied Sciences, Swansea University, Wales, UK. a.larimi@swansea.ac.uk.
⁴ Chemistry and Process Research Department, Niroo Research Institute, Tehran, Iran. aasgharinezhad@nri.ac.ir.

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-H₂/L) capacities, demonstrating the power of ML-accelerated screening for next-generation hydrogen storage materials.

Keywords: Artificial neural networks (ANNs); Grand canonical Monte Carlo (GCMC); Hydrogen storage; Metal–organic-framework (MOFs); Temperature–pressure swing conditions.