The latent representations of the cavities of the porous organic cage molecules describe how each cage is composed of the eigencages and are the rows of U ν Σ ν (see Fig. ). Because the latent space is of too high of a dimension to visualize directly (ν = 22), we resort to t-Distributed Stochastic Neighbor Embedding (t-SNE) to embed our learned latent space of porous cages into a two-dimensional space for visualization. In contrast to simply visualizing the first two dimensions of our latent space-essentially choosing ν = 2 in eqn. 6t-SNE is a non-linear dimensionality reduction algorithm and hence complicated structure in the ν = 22-dimensional space can be retained/captured in the 2D embedding. Moreover, while SVD prioritizes maintaining large distances in data space in the low-dimensional space, t-SNE is designed to preserve the local structure of the high-dimensional data in the lowdimensional embedding, but it also often captures global structure. A tunable parameter in t-SNE is the perplexity, a loose measure of how many neighbors each data point has. We find that our clusters of cages are fairly robust to our choice of the perplexity (5, in the range recommended in Ref. ). Fig. shows the resulting 2D embedding of the 3D porosity images of the porous cages; each cage appears as a point in this visualization of our latent cage space.