Predicting superconducting temperatures with new hierarchical neural network AI model

Abstract

Superconducting critical temperature is the most attractive material property due to its impact on the applications of electricity transmission, railway transportation, strong magnetic fields for nuclear fusion and medical imaging, quantum computing, etc. The ability to predict its value is a constant pursuit for condensed matter physicists. We developed a new hierarchical neural network (HNN) AI algorithm to resolve the contradiction between the large number of descriptors and the small number of datasets always faced by neural network AI approaches to materials science. With this new HNN-based AI model, a much-increased number of 909 universal descriptors for inorganic compounds, and a dramatically cleaned database for conventional superconductors, we achieved high prediction accuracy with a test R² score of 95.6%. The newly developed HNN model accurately predicted T_{\mathrm{c}} of 45 new high-entropy alloy superconductors with a mean absolute percent error below 6% compared to the experimental data. This demonstrated a significant potential for predicting other properties of inorganic materials.

Graphical abstract

Keywords

conventional superconducting critical temperature / hierarchical neural network / universal descriptors / artificial intelligence

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Declarations

The authors declare no competing interests and no conflicts.

Data & code availability

The code and cleaning data used to generate the results in this work can be downloaded from github.com/Dingfei1361/Conventional-SC-HNN-including-dataset/tree/main. The code can be successfully run once the Jupyter Notebook (Version 5.5.0) with the PyTorch package (Version 1.9.0+cu111) is set up.

Electronic supplementary materials

The online version contains supplementary material available at https://doi.org/10.15302/frontphys.2025.014204.

Acknowledgements

The authors acknowledge the funding support from the National Key R&D Program of China (Grant No. 2022YFB3807700), the Shenzhen Fundamental Research Funding (Nos. JCYJ20220818100612027 and JCYJ20220818100613028), and the Major Science and Technology Infrastructure Project of Shenzhen Material Genome Big-Science Facilities Platform.

RIGHTS & PERMISSIONS

2024 Higher Education Press