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.
@article{arxiv.2302.09337,
title = {Revealing intrinsic vortex-core states in Fe-based superconductors through machine-learning-driven discovery},
author = {Yueming Guo and Hu Miao and Qiang Zou and Mingming Fu and Athena S. Sefat and Andrew R. Lupini and Sergei V. Kalinin and Zheng Gai},
journal= {arXiv preprint arXiv:2302.09337},
year = {2023}
}