摘要
We develop InvDesFlow, an artificial intelligence (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-Tc superconductors. Utilizing InvDesFlow, we have obtained 74 thermodynamically stable materials with critical temperatures predicted by the AI model to be Tc ≥ 15 K based on a very small set of samples. Notably, these materials are not contained in any existing dataset.