Prediction of O2/N2 Selectivity in Metal–Organic Frameworks via High-Throughput Computational Screening and Machine Learning

Machine learning (ML), which is becoming an increasingly popular tool in various scientific fields, also shows the potential to aid in the screening of materials for diverse applications. In this study, the computation-ready experimental (CoRE) metal–organic framework (MOF) data set for which the O2 and N2 uptakes, self-diffusivities, and Henry’s constants were calculated was used to fit the ML models. The obtained models were subsequently employed to predict such properties for a hypothetical MOF (hMOF) data set and to identify structures having a high O2/N2 selectivity at room temperature. The performance of the model on known entries indicated that it would serve as a useful tool for the prediction of MOF characteristics with r2 correlations between the true and predicted values typically falling between 0.7 and 0.8. The use of different descriptor groups (geometric, atom type, and chemical) was studied; the inclusion of all descriptor groups yielded the best overall results. Only a small number of entries surpassed the performance of those in the CoRE MOF set; however, the use of ML was able to present the structure–property relationship and to identity the top performing hMOFs for O2/N2 separation based on the adsorption and diffusion selectivity.