  Finding new superconductors with a high critical temperature ($T_c$) has been
a challenging task due to computational and experimental costs. We present a
diffusion model inspired by the computer vision community to generate new
superconductors with unique structures and chemical compositions. Specifically,
we used a crystal diffusion variational autoencoder (CDVAE) along with
atomistic line graph neural network (ALIGNN) pretrained models and the Joint
Automated Repository for Various Integrated Simulations (JARVIS)
superconducting database of density functional theory (DFT) calculations to
generate new superconductors with a high success rate. We started with a DFT
dataset of $\approx$1000 superconducting materials to train the diffusion
model. We used the model to generate 3000 new structures, which along with
pre-trained ALIGNN screening results in 61 candidates. For the top candidates,
we performed DFT calculations for validation. Such approaches go beyond the
funnel-like materials design approaches and allow for the inverse design of
next-generation materials.
