We also use t-SNE to examine the learned feature space of the graph neural networks. Here we saw both encouraging and discouraging signs for the graph representations examined here. On the one hand, angle featurization helped distinguish between two very similar packing polymorphs with almost degenerate bandgap values. But on the other hand, the models did struggle with differentiating between polymorphs with varied packing motifs in embedding space. There seems to be room for improvement in the description of these materials via graph representation.