This study presents a prediction of the exchange Gibbs free energy at 1 bar and 298 K, which represents an energetic interpretation of the ambient-pressure selectivity (Equation ), using geometrical, chemical and energy descriptors presented in Tables and. The most correlated descriptor is the exchange Gibbs free energy at infinite dilution and 298 K. We will begin by studying the correlation between these two quantities since the exchange energy calculated by the GrAED algorithm already gives a very fast first evaluation of the selectivity. As shown in a previous study, 18 the difference of values between the low-pressure and ambient-pressure case is mainly a selectivity drop effect due to the near-saturation loading of adsorbates in the nanoporous material. To improve the accuracy of the evaluation, we train a model that integrates features that could help detect and quantify the selectivity drop that affects some highly selective materials. The ML model uses computationally cheaper descriptors to predict the computationally expensive ambient-pressure selectivity. Finally, we interpret the model to see how each feature contributed to the improved prediction compared to the simple infinite-dilution baseline.