In addition to the use of variational autoencoders, generative adversarial networks (GAN) have also been employed for the generation of material crystals. The focus in the work done by Kim et al. was on the generation of Mg-Mn-O ternary crystals using a point cloud representation which was a combination of unit cell parameters and fractional coordinates of the elements present in the crystals. These are concatenated to create a 2D matrix. This is useful as it has less memory requirement (by a factor of 400) in comparison to the iMatGen. Data augmentation was used to overcome the lack of translational, rotational and supercell invariances. The representation used in this work was similar to the one we introduce in the current work. But it differs in the sense that it was constrained by the composition of the materials which could be generated, e. Another kind of representation used for a General Adversarial Network is the translation of lattice constants and atomic positions into a voxel space, followed by encoding into a 2D crystal graph using an autoencoder. This helps change the heterogenous and discontinuous representation in the CIF files to a continuous and homogeneous representation. Long et al. used this representation to create stable crystal structures of multicomponent systems based on their formation energy.