Hydrogen storage metal-organic framework classification models based on crystal graph convolutional neural networks

Metal-organic frameworks (MOFs) have been considered as promising physical adsorbents for hydrogen storage due to their high porosity and structural tunability. We selected 7643 real MOFs from the computation-ready MOF 2019 database to screen high-performance materials for hydrogen storage based on the grand canonical Monte Carlo (GCMC) simulations. Based on the obtained data set, we proposed a deep learning classification model powered by the crystal graph convolutional neural networks (CGCNN) for the discovery of the optimal hydrogen storage MOFs. It is demonstrated that our classification model based on CGCNN algorithms exhibits a high prediction accuracy with the area under the ROC-plot curve (AUC) of 0.9208 for hydrogen storage performance of volumetric deliverable capacity (VDC). Compared with the classification models based on other machine learning algorithms such as random forest, our CGCNN model demonstrates the advantages of fast prediction with no feature extraction and little accuracy loss. When the trained CGCNN model was used to predict the classification of the unfamiliar samples (the randomly selected 1000 MOFs from the 137,953 hypothetical MOF database), we also obtain high accuracy with AUC > 0.81, indicating that this model exhibits reliable transferability for other types of MOFs. Meanwhile, we also elucidated the relationship between structure and performance of MOFs for hydrogen storage using the decision tree algorithms and quantitative structure–property analysis. Furthermore, the hydrogen adsorption performance and mechanism of top-performance MOFs were analyzed by adsorption isotherms, radial distribution functions, and mass center density distribution of equilibrium configurations. All those insights from atomic simulations and machine learnings can accelerate the discovery of new nanoporous materials not only for gas adsorption in MOFs but also for gas separation in other types of porous materials.