To summarize, while crystal graphs provide higher flexibility in handling input crystal size compared to other representations, their inherent non-invertibility renders them unsuitable for inverse design applications. Alternative representations like voxelized electron density maps are currently constrained to handling cubic structures, whereas iMatGen is memory intensive relative to 2D representations. In this work, we develop a new representation compatible with a CVAE, which puts no restrictions on the type of elements or the crystal structure. We use a CVAE and train it on our dataset to create a multidimensional latent space, which also has the values of formation energy encoded in one of its dimensions. The use of CVAE helps us generate new targeted stable or unstable crystals. We verify the results of CVAE using a neural network, which predicts the formation energy of the generated crystal structures. This is done to reduce the dependence on computationally expensive DFT calculations. In the end, we also use DFT calculations on a subset of our generated crystals to verify our results.