Fortunately, machine learning (ML) and more specifically deep learning (DL) methods, have emerged as powerful complements to the human capacity for materials design . Within the field of material discovery, generative ML models are currently being investigated by a wide variety of research teams . Previously, the two most common types of generative ML models were the VAE and GANs. VAEs attempt to encode a sample of data into a lower dimensional latent space. The encoded samples are then decoded from latent space into potential new samples. However, VAEs differ from traditional auto-encoders because they are simultaneously attempting to structure the latent space according to a predefined probability distribution. This makes VAEs an exciting candidate for efficient inverse design because materials that exhibit ideal properties may be located at the intersection of each property within latent space .