  Effective computational search holds great potential for aiding the discovery
of High-Temperature Superconductors (HTSs), especially given the lack of
systematic methods for their discovery. Recent progress has been made in this
area with machine learning, especially with deep generative models, which have
been able to outperform traditional manual searches at predicting new
superconductors within existing superconductor families but have yet to be able
to generate completely new families of superconductors. We address this
limitation by implementing conditioning -- a method to control the generation
process -- for our generative model and develop SuperDiff, a Denoising
Diffusion Probabilistic Model (DDPM) with Iterative Latent Variable Refinement
(ILVR) conditioning for HTS discovery -- the first deep generative model for
superconductor discovery with conditioning on reference compounds. With
SuperDiff, by being able to control the generation process, we were able to
computationally generate completely new families of hypothetical
superconductors for the very first time. Given that SuperDiff also has
relatively fast training and inference times, it has the potential to be a very
powerful tool for accelerating the discovery of new superconductors and
enhancing our understanding of them.
