Unlike the majority of selectivity predictions in the literature, the decision was made to predict selectivity on a logarithmic scale that focuses on the order of magnitude rather than the exact value of selectivity for highly selective materials. Moreover, converting to an exchange Gibbs free energy allowed for a more thermodynamic approach based on enthalpy, entropy, and free energy values. The challenge consisted of predicting the free energy equivalent of ambient-pressure selectivity using low-pressure selectivity alongside key energy, geometrical and chemical descriptors. The resulting fully optimized ML model exhibited high performance, yielding an RMSE of 0.37 kJ mol -1 , which corresponds to an RMSE of 0.06 on the base-10 log of selectivity. This represented an improvement compared to the 0.81 kJ mol -1