Abstract
In type-II superconductors, electronic states within magnetic vortices hold crucial information about the paring mechanism and can reveal non-trivial topology. While scanning tunneling microscopy/spectroscopy (STM/S) is a powerful tool for imaging superconducting vortices, it is challenging to isolate the intrinsic electronic properties from extrinsic effects like subsurface defects and disorders. Here we combine STM/STS with basic machine learning to develop a method for screening out the vortices pinned by embedded disorder in iron-based superconductors. Through a principal component analysis of large STS data within vortices, we find that the vortex-core states in Ba(Fe0.96Ni0.04)2As2 start to split into two categories at certain magnetic field strengths, reflecting vortices with and without pinning by subsurface defects or disorders. Our machine-learning analysis provides an unbiased approach to reveal intrinsic vortex-core states in novel superconductors and shed light on ongoing puzzles in the possible emergence of a Majorana zero mode.
| Original language | English |
|---|---|
| Article number | 045004 |
| Journal | 2D Materials |
| Volume | 11 |
| Issue number | 4 |
| DOIs | |
| State | Published - Oct 2024 |
Funding
This work was supported by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Division of Materials Sciences and Engineering and the STM experiment was conducted at the Center for Nanophase Materials Sciences (CNMS), which is a US Department of Energy, Office of Science User Facility at Oak Ridge National Laboratory.
Keywords
- Fe based superconductor
- machine learning driven research
- scanning probe microscopy
- vortexes