MOF H₂ Storage Predictor: A Deep Learning–Driven Hydrogen Storage Prediction Tool
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MOF H₂ Storage Predictor is a machine-learning–based tool designed to predict hydrogen storage capacity in metal–organic frameworks (MOFs).
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c2d — Chat to Dataset
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Multi-Level Structural Descriptors for Deep Learning–Based Prediction of Hydrogen Storage in Metal–Organic Frameworks
Published in Applied Sciences, 2026
A hybrid deep-learning framework using geometric, atomic, and crystallographic descriptors to predict hydrogen uptake in MOFs, used to screen 137,953 hypothetical MOFs and identify 630 candidates exceeding 50 g H2/L.
Recommended citation: Bai, Y., Aldrich, C., & Liu, X. (2026). "Multi-Level Structural Descriptors for Deep Learning–Based Prediction of Hydrogen Storage in Metal–Organic Frameworks." Applied Sciences, 16(19), 9574. https://doi.org/10.3390/app16199574
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