Prediction of Hydrogen Adsorption and Moduli of Metal–Organic Frameworks (MOFs) Using Machine Learning Strategies

Nicole Kate Borja, Christine Joy E. Fabros and Bonifacio T. Doma ()
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Nicole Kate Borja: School of Chemical, Biological, and Materials Science and Engineering, Mapua University, Manila 1002, Philippines
Christine Joy E. Fabros: School of Chemical, Biological, and Materials Science and Engineering, Mapua University, Manila 1002, Philippines
Bonifacio T. Doma: School of Chemical, Biological, and Materials Science and Engineering, Mapua University, Manila 1002, Philippines

Energies, 2024, vol. 17, issue 4, 1-14

Abstract: For hydrogen-powered vehicles, the efficiency cost brought about by the current industry choices of hydrogen storage methods greatly reduces the system’s overall efficiency. The physisorption of hydrogen fuel onto metal–organic frameworks (MOFs) is a promising alternative storage method due to their large surface areas and exceptional tunability. However, the massive selection of MOFs poses a challenge for the efficient screening of top-performing MOF structures that are capable of meeting target hydrogen uptakes. This study examined the performance of 13 machine learning (ML) models in the prediction of the gravimetric and volumetric hydrogen uptakes of real MOF structures for comparison with simulated and experimental results. Among the 13 models studied, 12 models gave an R 2 greater than 0.95 in the prediction of both the gravimetric and the volumetric uptakes in MOFs. In addition, this study introduces a 4-20-1 ANN model that predicts the bulk, shear, and Young’s moduli for the MOFs. The machine learning models with high R 2 can be used in choosing MOFs for hydrogen storage.

Keywords: hydrogen uptakes in metal–organic frameworks (MOFs); machine learning algorithms; modulus prediction of MOFs (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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