Journal Article

Developing a complete AI-accelerated workflow for superconductor discovery

Li, Zhongwei, Stewart, Gregory R., Hennig, Richard G., Geisler, Benjamin, Hamlin, James J., Hire, Ajinkya C., Kim, Jung Soo, Dee, Philip M., Gibson, Jason B., Prakash, Pawan, Hirschfeld, P. J.

2026

Overview

The quest to identify new superconducting materials with enhanced properties is hindered by the prohibitive cost of computing electron-phonon spectral functions, severely limiting the materials space that can be explored. Here, we introduce a Bootstrapped Ensemble of Equivariant Graph Neural Networks (BEE-NET), a machine-learning model trained to predict the Eliashberg spectral function and superconducting critical temperature with a mean-absolute-error of 0.87 K relative to DFT-based Allen-Dynes calculations. Intriguingly, BEE-NET achieves a true-negative-rate of 99.4%, enabling highly efficient screening for the rare property of superconductivity. Integrated into a multi-stage, AI-accelerated discovery pipeline that incorporates elemental-substitution strategies and machine-learned interatomic potentials, our workflow reduced over 1.3 million candidate structures to 741 dynamically and thermodynamically stable compounds with DFT-confirmed T c > 5 K. We report the successful synthesis and experimental confirmation of superconductivity in two of these previously unreported compounds. This study establishes a data-driven framework that integrates machine learning, quantum calculations, and experiments to systematically accelerate superconductor discovery.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 639/301 / 639/766 / Characterization and Evaluation of Materials / Chemistry and Materials Science / Computational Intelligence / Crystal structure / Electrons / Graph neural networks / Learning algorithms / Machine learning / Materials Science / Mathematical and Computational Engineering / Mathematical and Computational Physics / Mathematical Modeling and Industrial Mathematics / Neural networks / Superconductivity / Theoretical / Workflow