  Despite having been discovered more than three decades ago, High Temperature
Superconductors (HTSs) lack both an explanation for their mechanisms and a
systematic way to search for them. To aid this search, this project proposes
ScGAN, a Generative Adversarial Network (GAN) to efficiently predict new
superconductors. ScGAN was trained on compounds in OQMD and then transfer
learned onto the SuperCon database or a subset of it. Once trained, the GAN was
used to predict superconducting candidates, and approximately 70\% of them were
determined to be superconducting by a classification model--a 23-fold increase
in discovery rate compared to manual search methods. Furthermore, more than
99\% of predictions were novel materials, demonstrating that ScGAN was able to
potentially predict completely new superconductors, including several promising
HTS candidates. This project presents a novel, efficient way to search for new
superconductors, which may be used in technological applications or provide
insight into the unsolved problem of high temperature superconductivity.
