Chemical-guided screening of top-performing metal–organic frameworks for hydrogen storage: An explainable deep attention convolutional model

Abstract

Metal-organic framework (MOF)-based adsorptive hydrogen storage holds promise for enhancing the sustainable design of hydrogen storages by enhancing the usable volumetric (UV) and gravimetric (UG) capacities. However, the extensive number of MOFs poses a challenge in the search for optimal materials owing to the lack of an efficient and interpretable high-throughput screening method. This study introduces an explainable artificial intelligence (XAI) framework to expedite the discovery of high-capacity hydrogen adsorbents by predicting the UV and UG capacities using an attention densely connected convolutional (ADCC) network. A new hybrid dataset with various operating conditions and comprising 24 physical–chemical descriptors, such as void fraction (VF) and metal percentage (MP), was utilized to develop the ADCC model. The explainable ADCC model demonstrated superior predictive performance for the UV and UG capacities, with R2 values of 0.9886 and 0.9982, respectively. The inclusion of the chemical descriptors MOFs enhanced the prediction accuracy of the ADCC model. The XAI analysis showed that VF and MP dominated physical and chemical descriptors, respectively, for UV and UG. Consequently, the ADCC model identified EFAYIU—a real MOF—as a promising hydrogen storage material with UV and UG capacities of 51.55 g H2/L and 11.37 wt%, respectively, surpassing the current materials for hydrogen storage. Additionally, the identified EFAYIU was validated based on molecular simulations, confirming the high hydrogen adsorptive capacities obtained by the ADCC model. Thus, the proposed AI-based high-throughput screening method enables the rapid discovery of high-performance MOFs for sustainable hydrogen storage.