ArticleScientific reports2025
White-box methodologies for achieving robust correlations in hydrogen storage with metal-organic frameworks.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Interpretable white-box modeling for nitrogen storage in metal-organic frameworks.Scientific reports · 2026Article
- White-box modeling of asphaltene precipitation during natural depletion of oil reservoirs.Scientific reports · 2026Article
- Predictive modelling and optimization of WEDM of nickel aluminium bronze alloy using optimised support vector regression and evolutionary algorithm.Scientific reports · 2025Article
- Toward accurate prediction of NScientific reports · 2025Article
- Pore Size Engineering of MOFs by Pore Edge Reaction: Tetrazine Click and Hydrogen Adsorption in Theory and Experiment.Chemistry of materials : a publication of the American Chemical Society · 2025Article
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7 authors.
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Abstract
Hydrogen is recognized as a clean energy replacement for non-renewable fossil fuels, and the utilization of metal-organic frameworks (MOFs) for hydrogen storage has gained considerable interest in recent years. In this study, hydrogen storage in MOFs was estimated using white-box methods, namely group method of data handling (GMDH), genetic programming (GP), and gene expression programming (GEP), which are robust soft-computing methods known for generating innovative correlations. To this end, temperature, pressure, pore volume, and surface area were implemented as input parameters for constructing these robust correlations. After that, the superiority of the established correlations was demonstrated through multiple statistical and graphical error assessment. The results indicated, the GMDH model demonstrates the highest accuracy with root mean square error (RMSE), and mean absolute error (MAE) values of 0.410 and 0.307, respectively. However, the GEP model's accuracy was comparable to that of the GMDH model. In addition, sensitivity assessment showed that the pore volume and the pressure exhibit the strongest linear and non-linear relationships, respectively, with the H
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