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Metal–organic frameworks (MOFs) are crystalline materials and one of the optimal materials for large-scale grand canonical Monte Carlo (GCMC) simulations. Recently, there have been trials for applying machine learning (ML) to the results of large-scale GCMC simulations to predict gas adsorption on MOFs. However, the functions of the developed algorithms are not different from those of GCMC simulations, in that they provide a prediction of adsorption properties based on the coordination structures. In this study, we propose a novel Monte Carlo-Machine Learning (MC-ML) strategy, which combines ML with GCMC to provide the function that is distinct from that of GCMC. To verify the concept of the strategy, we designed an algorithm to predict methane isotherms at a range of temperatures from a methane isotherm at a temperature of 298 K. GCMC simulations functioned as a data-producing tool for ML, which yielded adsorption properties of 4951 structures in the CoRE-MOF database. The ML was applied to the GCMC results using experimentally measurable properties as features. Finally, the algorithm developed from ML was evaluated using experimental methane adsorption data for defective MOFs, MOFs with open metal sites, and non-MOF materials, which revealed the merits of the MC-ML strategy in comparison with typical GCMC.

Porous carbon materials were synthesized by pyrolysis of zinc-based MOFs. These materials exhibited superior CO2 capacities and better CO2 separation ability under humid conditions compared to those of the pristine MOFs.