InvDesFlow: An AI-driven materials inverse design workflow to explore possible high-temperature superconductors
This work addresses the challenge of accelerating the discovery of high-temperature superconductors for materials science and condensed matter physics, representing a novel application of AI rather than an incremental improvement.
The researchers tackled the problem of discovering new high-temperature superconducting materials by developing InvDesFlow, an AI-driven workflow that integrates deep learning and physics-based methods, resulting in the identification of 74 dynamically stable materials with predicted critical temperatures of at least 15 K, including specific examples like B$_4$CN$_3$ at 24.08 K and B$_5$CN$_2$ at 15.93 K.
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.