The above MLP demonstrates the concept of a ML-based universal adsorption model to be sound. Yet, the feasibility of a truly universal model is contingent on the ability to incorporate sufficient adsorption data for molecules (real or alchemical) with more diverse sizes, shapes, polarity, and flexibility. However, the above MLP required approximately 5 million GCMC data points, encompassing adsorption data for 200 small, non-polar, near-spherical, rigid alchemical adsorbates, on a relatively small set of 1,800 topologically and chemically diverse ToBaCCo 32 -generated MOFs, at fugacities of 0.01, 0.025, 0.05, 0.075, 0.1, 0.25, 0.5, 0.75, 1, 5, 10, 50, 75, and 100 bar . Overall, each MOF required about 2,800 adsorption data points for training.