Several models from each category (Vanilla GANs, WGANs, and Diffusion Models) were trained using the CrysTens representations in an attempt to understand which deep learning method should be explored further for material discovery. The models were evaluated on several different metrics and we found a strong correlation between a general "understanding" of our representation and the output CIFs that were received. The GAN and WGAN used were custom convolution-based models. The Diffusion Model is the model found at imagen-pytorch. The details and hyperparameters of each model can be found in Appendix A.