Deep Learning Based Superconductivity: Prediction and Experimental Tests
Abstract
The discovery of novel superconducting materials is a longstanding challenge in materials science, with a wealth of potential for applications in energy, transportation, and computing. Recent advances in artificial intelligence (AI) have enabled expediting the search for new materials by efficiently utilizing vast materials databases. In this study, we developed an approach based on deep learning (DL) to predict new superconducting materials. We have synthesized a compound derived from our DL network and confirmed its superconducting properties in agreement with our prediction. Our approach is also compared to previous work based on random forests (RFs). In particular, RFs require knowledge of the chem-ical properties of the compound, while our neural net inputs depend solely on the chemical composition. With the help of hints from our network, we discover a new ternary compound , which becomes superconducting below 5.4 K. We further discuss the existing limitations and challenges associated with using AI to predict and, along with potential future research directions.
Cite
@article{arxiv.2412.13012,
title = {Deep Learning Based Superconductivity: Prediction and Experimental Tests},
author = {Daniel Kaplan and Adam Zhang and Joanna Blawat and Rongying Jin and Robert J. Cava and Viktor Oudovenko and Gabriel Kotliar and Anirvan M. Sengupta and Weiwei Xie},
journal= {arXiv preprint arXiv:2412.13012},
year = {2025}
}
Comments
14 pages + 2 appendices + references. EPJ submission