Inverse Design of Next-Generation Superconductors Using Data-Driven Deep Generative Models
Inverse Design of Next-Generation Superconductors Using Data-Driven Deep Generative Models
Finding new superconductors with a high critical temperature (Tc) 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) …