As a faster alternative to Monte Carlo simulations, ML-based models have recently been developed and are able to accurately reproduce the Monte Carlo results . However, training these models requires a substantial amount of data, usually coming from GCMC simulations, involving a time-intensive phase of training data generation. Furthermore, as the ML-based methods are not physics-based, these models are usually trained on one property in specific conditions, such as the loading at a defined set of pressures for fixed temperature or the enthalpy of adsorption at one pressure and one temperature .