Finally, to investigate whether the correlation between CrysTens symmetry and CIF quality was reflected on an external metric, we used the Crystal Graph Convolutional Neural Network (CGCNN) to calculate the distribution of predicted formation energy, final energy, band gap, bulk moduli, shear moduli, Fermi Energy, and Poisson Ratio for the real CIFs as well as the generated CIFs for each model (Fig. ) . We also used CGCNN to calculate decomposition energy using the model found at . com/sparks-baird/mat discover/tree/ cgcnn-decomposition-energy/models/ CGCNN. Finally, several generated CIFs from each model are shown to showcase the visual differences in the generated crystal quality (Fig. , Fig. , and Fig. ). The produced CrysTens representations of the crystals shown, used 3-Means Clustering for the atomic numbers and 6-Means Clustering for the potential distinct x, y, and z coordinates. The difference between the "absolute" x, y, and z values predicted and the "relative" position predicted by the direction graph. 2 The sum of the difference between the reconstructed pairwise distance matrix (by the final coordinates) and the generated pairwise distance matrix in Layer 1 of CrysTens.