Explore generative machine learning approaches for materials discovery in this seminar by Prof. Taylor D. Sparks from the University of Utah. Delve into the potential of machine learning to uncover novel materials that differ chemically and structurally from known examples. Examine generative models like variational autoencoders, generative adversarial networks, and diffusion models, comparing their applications in materials science to image generation. Investigate the unique challenges of generating periodic crystalline structures using these tools. Learn about the Descending from Stochastic Clustering Variance Regression (DiSCoVeR) algorithm, designed to guide the discovery process towards promising yet unintuitive material candidates. Gain insights into the future of materials discovery, including discussions on GAN architecture, encoding distances, uncertainty, and potential next steps in the field.