When computationally scanning the cage molecule to generate the 3D porosity image, we binarized each pixel as void space or overlapping the cage structure using helium as a probe. This ensures that the 3D porosity image only contains information about the shape of the cage molecule and its cavity. Another option to likely lend a more predictive feature for a regression model of adsorption of a particular gas is to instead assign the pixel value to be the free energy of adsorption of that gas centered at that point. For this study, we elected to binarize the pixels to yield a more generalizable latent representation and avoid overfitting the latent representation to describe a particular gas.