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 R2 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 R2 can be used in choosing MOFs for hydrogen storage.
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