Recently, our group proposed a methodology for the identification of separation selectivity at different operating conditions (temperature and pressure) on a large set of nanoporous structures. To avoid the necessity for systematic GCMC calculations, at least in a first phase of the screening process, we developed a screening process (illustrated in Figure ) based a machine learning predictor of selectivity, which is fed by both geometrical descriptors as well as energy-relation properties, obtained by a fast grid calculation of host-guest interaction energies. As a proof of concept, we tested our methodology for the separation of a 20:80