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

Abstract

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 $T_c\ge$ 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.

arXiv comment22 pages, 17 figures, 6 tables

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Cited by in corpus (6)

• InvDesFlow-AL: active learning-based workflow for inverse design of functional materials
• High-temperature superconductivity in Li${}_2$AuH${}_6$ mediated by strong electron-phonon coupling under ambient pressure
• CrystalFlow: A Flow-Based Generative Model for Crystalline Materials
• Charting the landscape of Bardeen-Cooper-Schrieffer superconductors in experimentally known compounds
• HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature Superconductors for AI-Driven Critical Temperature Prediction
• Reinforcement Fine-Tuning for Materials Design