InvDesFlow: An AI-driven materials inverse design workflow to explore possible high-temperature superconductors

The discovery of new superconducting materials, particularly those exhibiting high critical temperature (T_c), has been a vibrant area of study within the field of condensed matter physics. Conventional approaches primarily rely on physical intuition to search for potential superconductors within the existing databases. However, the known materials only scratch the surface of the extensive array of possibilities within the realm of materials. Here, we develop InvDesFlow, an AI-driven materials inverse design workflow that integrates deep model pre-training and fine-tuning techniques, diffusion models, and physics-based approaches (e.g., first-principles electronic structure calculation) for the discovery of high-T_c superconductors. Utilizing InvDesFlow, we have obtained 74 thermodynamically stable materials with critical temperatures predicted by the AI model to be T_c ≥ 15 K based on a very small set of samples. Notably, these materials are not contained in any existing dataset. Furthermore, we analyze trends in our dataset and individual materials including B₄CN₃ (at 5 GPa) and B₅CN₂ (at ambient pressure) whose T_cs are 24.08 K and 15.93 K, respectively. We demonstrate that AI technique can discover a set of new high-T_c superconductors, outline its potential for accelerating discovery of the materials with targeted properties.