Geometrical Properties Can Predict CO2 and N2 Adsorption Performance of Metal-Organic Frameworks (MOFs) at Low...

Metal-organic frameworks (MOFs) are nanoporous materials with exceptional host-guest properties poised for groundbreaking innovations in gas separation applications according to high-throughput (HT) screening data. However, MOF structural libraries are nearly infinite in practice and so statistical and information technology will play a fundamental role in implementing and rationalizing MOF virtual screening. In this work, we apply k-means clustering and archetypal analysis (AA) to identify the truly significant nanoporous structures in a large library of ∼82 000 virtual MOFs. Quantitative structure-property relationship (QSPR) models of the theoretical CO2 and N2 uptake capacities were also developed using a calibration set of ∼16 000 hypothetical MOF structures derived from the prototypes and archetype frameworks. Since uptake capacities correlated poorly to the void fraction, surface area and pore size but these properties were used to build binary classifier predictors that successfully identify "high-performing" nanoporous materials in an external test set of ∼65 000 MOFs with accuracy higher than 94%. The accuracy of the classification decreased for MOFs with fluorine substituents. The classification models can serve as efficient filtering tools to detecting promising high-performing candidates at the early stage of virtual high-throughput screening of novel porous materials.