There are questions that naturally arise when one is first introduced to this representation. One of which is, "Why is each layer 64x64?" The reason that we selected 64 as the length and width of each of our layers is because of the commonly used deep learning heuristic of selecting powers of two. It could easily have been another value, however, the focus of this work is to show the structure of the CrysTens representation rather than the numerical intricacies or optimization of the representation. Furthermore, since 64 was selected and there are eleven different parameters plus a padding layer, only crystal structures with 52 atoms or less were selected for training and analysis. Another question that may arise is "Why use four layers?" In this work, our goal was to fully capture all of the different structural aspects of a given crystal which is why we redundantly included both the pairwise distance matrix and the three-dimensional graphs, however, the representation can be changed to fit given constraints. We anticipate that future work could add layers to encode aspects related to chemistry such as the Oliynyk, magpie, mat2vec vector constituents in order to ensure realistic atom assignment.