Examining a number of research papers on supervised ML for the prediction of adsorption properties, we identify a few recurrent descriptors: (i) geometrical descriptors obtained using software like Zeo++ 24 including surface area (SA), void fraction (VF), largest cavity diameter (LCD) and pore limiting diameter (PLD); and (ii) physical and chemical descriptors such as framework density, framework molar mass, percentage of carbon (C%), nitrogen (N%), oxygen (O%), hydrogen, as well as halogen, nonmetals, metalloids and metals, and degree of unsaturation. Although these descriptors are versatile and widely used in ML models, they fail to provide specific information for the ML task of this study. As demonstrated by Simon et al., energy descriptors greatly influence ML models for selectivity prediction.