Similar to Ref , we used t-distributed stochastic neighbor embedding (t-SNE) to visualize and evaluate the graph-level embedding from the pooling layer. The results are shown in Figure . Figure show the OCELOT-trained models with (a) and without (b) angle featurization. Figure show the OMDB-trained models with (c) and without (d) angle featurization. Figure show OMDB-trained models fine-tuned on OCELOT with (e) and without (f) angle featurization. Darker colors correspond to lower bandgaps and lighter colors correspond to higher bandgaps. The t-SNE visualization represents the latent space for the trained materials; points in a group should share structural similarities. As can be seen, Figure , the models with angle featurization are able to cluster structures according to their bandgap; the low bandgap materials reside in the top right portion of the feature space and lower bandgap materials reside in the bottom half. Models without angle featurization had a much more mixed blend of bandgaps throughout the feature space. However, Figure does have some clustering for lower bandgap values. Rather than clustering in the bottom half of feature space, however, these materials reside in the top right portion. Outside of this portion of the feature space, there is not much clustering according to bandgap.