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

TL;DR

This paper presents a machine learning approach to determine the pairing symmetry in unconventional superconductors using quasiparticle interference data, combining theoretical modeling and symmetry classification to accurately identify superconducting gap structures.

Contribution

The study introduces a novel machine-learning-guided method to analyze QPI data for identifying pairing symmetries, demonstrated on monolayer NbSe2, integrating first-principles calculations and symmetry analysis.

Findings

High accuracy in identifying pairing channels from QPI data

QPI contains rich, learnable information about superconducting gaps

Method applicable to real materials like monolayer NbSe2

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.