Inverse Design of Novel Superconductors via Guided Diffusion

The inverse design of materials with specific desired properties, such as high-temperature superconductivity, represents a formidable challenge in materials science due to the vastness of chemical and structural space. This work introduces a guided diffusion framework to accelerate the discovery of novel superconductors. Our approach begins with a foundation model trained on crystal structures from the Alexandria Database, which is then fine-tuned using a labeled dataset of 7,217 conventional superconductors to generate new structures conditioned on critical temperature, $T_\mathrm{c}$. Employing classifier-free guidance, we generated 200,000 potential crystal structures. These candidates were subsequently subjected to a rigorous multi-stage computational screening workflow, utilizing machine learning models and density functional theory calculations to assess stability and electronic properties. Notably, our generative model demonstrated effective property-driven design by shifting the distribution of generated materials toward targeted $T_\mathrm{c}$ values within the training regime. This process successfully identified 773 promising superconducting candidates with predicted $T_\mathrm{c}>5K$. This end-to-end workflow, from generation to new candidate superconductors, illuminates a powerful pathway for materials discovery, demonstrating the significant potential of the AI-driven framework to accelerate discovery.