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Machine learning protocol to identify pairing symmetries via quasiparticle interference imaging in Ising superconductors

Adam Hložný1,2, Jozef Haniš3, Martin Gmitra3,4,5, and Marko Milivojević1,6,*

  • *Contact author: marko.milivojevic@savba.sk
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Phys. Rev. B 114, 024511 – Published 21 July, 2026

DOI: https://doi.org/10.1103/7zvs-rnmt

Abstract

Identifying the pairing symmetry in unconventional superconductors is essential for reliably characterizing their superconducting states and for enabling their integration into realistic quantum devices. Here, we introduce a machine-learning-guided strategy to determine pairing symmetry from quasiparticle interference (QPI) data, which integrates first-principles calculations, tight-binding modeling, and symmetry-based classification of the superconducting pairing function. We demonstrate the approach on monolayer NbSe2 as an experimentally accessible probe of superconductivity in real materials, within a single scalar-impurity Bogoliubov–de Gennes framework. Our analysis shows that the QPI-to-parameter inverse problem can be solved with high accuracy for most superconducting pairing channels in this setting, indicating that QPI carries rich, learnable information about the superconducting gap structure. Taken together, these results demonstrate that machine-learning-assisted QPI analysis provides a promising pathway for precise learning of superconducting pairing functions in quantum materials.

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Phys. Rev. B 114, 024511– Published 21 July, 2026
  • Received 30 March 2026
  • Revised 8 June 2026
  • Accepted 6 July 2026
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