Furthermore, data on adsorption in porous materials is sparse in the literature, as some materials have been specifically studied with a limited the number of gases adsorbed. This is why computational methods have long been used in order to supplement experimental databases -either through systematic studies, or more recently by using a machine learning-based recommendation system. To this end, Grand Canonical Monte Carlo (GCMC) simulations have been used extensively to predict fluid uptake and gas separation. Within a limited set of assumptions (rigid adsorbent, accurate classical force field, no open metal sites), these predictions are relatively accurate and computationally cheap, providing absolute gas uptake values in the case of single adsorption, or co-adsorption uptakes at a fixed temperature and mixture composition. Therefore GCMC data have been used in many screening studies on MOFs, and are particularly useful for co-adsorption characterization, where experiments are particularly difficult and the dimensionality of the thermodynamic space is higher.