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

arXiv: https://arxiv.org/abs/2409.08065 · HTML: https://mlsanity.com/paper/2409.08065/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2024.

## Built on by (most cited first, 6 of 6)

- [Reinforcement Fine-Tuning for Materials Design](https://mlsanity.com/paper/2504.02367/) (2025)
- [TopoMAS: Large Language Model Driven Topological Materials Multiagent System](https://mlsanity.com/paper/2507.04053/) (2025)
- [InvDesFlow-AL: active learning-based workflow for inverse design of functional materials](https://mlsanity.com/paper/2505.09203/) (2025)
- [High-temperature superconductivity in Li2AuH6 mediated by strong electron-phonon coupling under ambient pressure](https://mlsanity.com/paper/2501.12222/) (2025)
- [Design Topological Materials by Reinforcement Fine-Tuned Generative Model](https://mlsanity.com/paper/2504.13048/) (2025)
- [InvDesMobility: a reliability-gated first-principles feedback framework for closed-loop materials discovery](https://mlsanity.com/paper/2606.16133/) (2026)

## Abstract (arXiv)

The discovery of new superconducting materials, particularly those exhibiting high critical temperature (Tc), 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-Tc superconductors. Utilizing InvDesFlow, we have obtained 74 dynamically 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. Furthermore, we analyze trends in our dataset and individual materials including B4CN3 (at 5 GPa) and B5CN2 (at ambient pressure) whose Tcs are 24.08 K and 15.93 K, respectively. We demonstrate that AI technique can discover a set of new high-Tc superconductors, outline its potential for accelerating discovery of the materials with targeted properties.