Training of a universal adsorption model implies presenting the model with adsorption data for different molecules, along with some representation of said molecules. As molecules can be modeled using some combinations of values for parameters in intermolecular (e.g., Lennard Jones and Coulombic parameters) and intramolecular potentials, Gómez-Gualdrón and coworkers showed that to "teach" a model about adsorption, one does not need to limit the adsorption data to real molecules. Specifically, they created 200 alchemical molecules using arbitrary combinations of potential parameters, obtained adsorption data for them using molecular simulation and used the data to train a multi-layer perceptron (MLP) model capable of predicting full isotherms for real, small, non-polar, near-spherical, rigid molecules.