Learning from Disorder in Superconductors with Scanning Probe Microscopy and Data Analytics

Disorder is a powerful approach to elicit and control superconducting properties, as evidenced from record Tc's, quantum phase transitions and exotic quasiparticles, predicted or evidenced in the presence of disorder. Here we will categorize disorder in notoriously heterogeneous scanning tunneling microscopy (STM) images of unconventional superconductors. From the information centric point of view, understanding the structure of these datasets amounts to effective and physically meaningful data compression. We will discuss compressibility of various kinds of STM data setting up the context for techniques of compressive sensing, machine learning and information theory as a way to understand the results of the experiments and improve data acquisition. Similarity learning emerges as a consistent strategy to categorize disorder with minimum prior knowledge. We also apply disorder analysis to identify Josephson and Andreev currents in on the atomic scale. Research sponsored by Materials Science and Engineering Division, US DOE and Center for Nanophase Materials Sciences, Oak Ridge National Lab, a DOE Office of Science user facility.<br/>1. P. Maksymovych, J. Yang, B. C. Sales, J. Wang, arxiv:2106.13691<br/>2. B. Lerner, A. Flores-Garibay, B. J. Lawrie, P. Maksymovych, Phys. Rev. Research <b>3</b> (2021) 43040<br/>3. W. Ko, E. Dumitrescu, P. Maksymovych, Phys. Rev. Research <b>3</b> (2021) 033248