VAEs have already shown promise in generating many kinds of complicated data, including handwritten digits, faces, physical models of scenes, segmentation, and predicting the future from static images. VAEs have also been used previously to generate crystal structures . They also give us the freedom to use different sampling techniques, which can be of help when trying to generate crystals similar to the ones already known , as the latent space near already encoded crystals are bound to have crystals with similar properties. The goal of the current work was generation of novel materials with a targeted property (here formation energy) via a purely unsupervised design method. To achieve this, we extend the VAE to a Conditional Variational Autoencoder (CVAE) and use our already existing database to label the crystals with formation energy greater than 0 and those smaller than 0.