We note that the work done by Ren et al. is similar to what we discuss in this paper. Their representation used the information in the CIF file to create a 2D input matrix. In addition, they also include the elemental properties by projecting them to different crystal planes using a discrete fourier transform. Similar to us they also use an autoencoder based model for learning the underlying distribution of the dataset and generating new crystal structures. The difference lies in our generation of crystals with some targeted properties. Their work makes use of local perturbation (a sampling technique) to generate crystals similar to the ones already present in the dataset. Whereas our model is able to randomly generate crystals with the desired property by sampling out from the appropriate dimension in the latent space. Furthermore, we note that our approach allows for greater flexibility for incorporating new sites and elements as the Ren et al. representation is currently limited up to ternary compounds.