Revealing intrinsic vortex-core states in Fe-based superconductors through machine-learning-driven discovery

TL;DR

This paper introduces a machine learning method combined with STM/S to isolate intrinsic vortex-core electronic states in Fe-based superconductors, helping to understand pairing mechanisms and topological features.

Contribution

It presents a novel unsupervised machine learning approach to distinguish intrinsic vortex states from extrinsic effects in superconductors.

Findings

Successfully isolates intrinsic vortex-core states

Reveals potential insights into Majorana zero modes

Provides an unbiased screening method

Abstract

Electronic states within superconducting vortices hold crucial information about paring mechanisms and topology. While scanning tunneling microscopy/spectroscopy(STM/S) can image the vortices, it is difficult to isolate the intrinsic electronic states from extrinsic effects like subsurface defects and disorders. We combine STM/S with unsupervised machine learning to develop a method for screening out the vortices pinned by embedded disorder in Fe-based superconductors. The approach provides an unbiased way to reveal intrinsic vortex-core states and may address puzzles on Majorana zero modes.