Enhancing Gas Separation Selectivity Prediction through Geometrical and Chemical Descriptors

Adsorption-based techniques for gas separation using nanoporous materials are widely used and hold a promising future, but systematic identification of the best-performing materials for a given application is still an open problem. For that task, we need to estimate selectivity at different operating conditions (temperature and pressure) on a large set of nanoporous structures. To this aim, we have developed a machine-learning-assisted screening process based on a fast grid calculation of interaction energies, in addition to newly designed geometrical descriptors to predict ambient-pressure selectivity. As a proof of concept, we tested our methodology for the separation of a 20:80 xenon/krypton mixture at 298 K and 1 atm in the nanoporous materials of the CoRE MOF 2019 database. Based on a train/test split of the data set, our model is promising with an RMSE of 2.5 on the ambient-pressure selectivity values of the test set and 0.06 on the log 10 of the selectivity. This method can thence be used to preselect the best performing materials for a more thorough investigation.