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 search engine 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 dynamically stable materials with critical temperatures predicted by the AI model to be $T_c \geq$ 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 B$_4$CN$_3$ (at 5 GPa) and B$_5$CN$_2$ (at ambient pressure) whose $T_c$s 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.

翻译：新型超导材料（尤其是具有高临界温度（$T_c$）的材料）的发现一直是凝聚态物理领域中一个活跃的研究方向。传统方法主要依赖物理直觉，在现有数据库中搜索潜在的超导体。然而，已知材料仅触及了材料领域广阔可能性中的表面部分。在此，我们开发了InvDesFlow，这是一种集成了深度模型预训练与微调技术、扩散模型以及基于物理的方法（例如第一性原理电子结构计算）的AI搜索引擎，用于发现高$T_c$超导体。利用InvDesFlow，我们基于极少量样本获得了74种动态稳定的材料，其临界温度经AI模型预测为$T_c \geq$ 15 K。值得注意的是，这些材料并未包含在任何现有数据集中。此外，我们分析了数据集中的整体趋势以及个别材料，包括B$_4$CN$_3$（在5 GPa压力下）和B$_5$CN$_2$（在常压下），其$T_c$分别为24.08 K和15.93 K。我们证明，AI技术能够发现一系列新型高$T_c$超导体，并概述了其在加速发现具有目标特性材料方面的潜力。