  Recent machine learning models to accurately obtain gas adsorption isotherms
focus on polymers or metal-organic frameworks (MOFs) separately. The difficulty
in creating a unified model that can predict the adsorption trends in both
types of adsorbents is challenging, owing to the diversity in their chemical
structures. Moreover, models trained only on single gas adsorption data are
incapable of predicting adsorption isotherms for binary gas mixtures. In this
work, we address these problems using feature vectors comprising only the
physical properties of the gas mixtures and adsorbents. Our model is trained on
adsorption isotherms of both single and binary mixed gases inside carbon
molecular sieving membrane (CMSM), together with data available from CoRE MOF
database. The trained models are capable of accurately predicting the
adsorption trends in both classes of materials, for both pure and binary
components. ML architecture designed for one class of material, is not suitable
for predicting the other class, even after proper training, signifying that the
model must be trained jointly for proper predictions and transferability. The
model is used to predict with good accuracy the CO2 uptake inside CALF-20
framework. This work opens up a new avenue for predicting complex adsorption
processes for gas mixtures in a wide range of materials.
