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Machine-learning prediction of superconductivity from high-temperature resistivity in Fe-based compounds

Yuchen Wu, Yiwen Liu, Wanyue Lin, Zohar Nussinov, and Sheng Ran*

  • *Contact author: rans@wustl.edu
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APS Open Sci. 1, 000053 – Published 26 June, 2026

DOI: https://doi.org/10.1103/r63k-rfdq

Abstract

Unconventional superconductivity remains one of the central unsolved problems in quantum materials, and revealing its connection to the normal state is widely believed to be key to uncovering the pairing mechanism. Previous efforts have largely focused on the temperature range immediately above the superconducting transition, where specific scattering channels—such as strange-metal transport—have been identified as sharing a possible microscopic origin with superconductivity. Here, we use machine learning to explore possible correlations between normal-state resistivity and superconductivity in Fe-based compounds. We find that resistivity data in the temperature window of 150–300 K, far above the typical critical temperature (Tc) of this family, can be used to classify whether a compound becomes superconducting. Owing to the limited size of the dataset, the underlying physical origin of this correlation cannot yet be determined. Nevertheless, the results suggest that high-temperature resistivity may contain useful empirical information for superconductivity prediction, motivating larger and more systematic experimental datasets.

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Information

APS Open Sci. 1, 000053– Published 26 June, 2026
Vol. 1
  • Received 20 January 2026
  • Accepted 17 June 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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