Navigating atomic-scale disorder with correlative tunneling microscopy and defect manipulation

Disorder is a powerful approach to elicit and control quantum properties in the bulk, as evidenced from record T_c, quantum phase transitions and exotic quasiparticles, predicted or evidenced in disordered superconducting and topological materials. Here we will introduce several new approaches to reveal, understand and introduce disorder in unconventional superconductors on the nanoscale, by augmenting scanning tunneling microscopy with machine learning, force microscopy and new defect manipulation methods. We will demonstrate reduced-dimensionality manifolds as a natural choice for sparse representation of heterogeneous disorder effects, with the potential to mirror the success of integral transforms for quantitative analysis of periodic structures. The techniques of metric analysis enables optimization of machine learning techniques to both amplify the disorder signatures in the hyperspectral data-sets, detect artefacts and reveal otherwise hidden properties of spectral weight transfer. Finding correlations within datasets can reveal the very mechanism of tunneling and imaging modes, such as statistically identifying the regime of Josephson tunneling in STM with a superconducting tip. Finally, correlative imaging of atomic-scale forces and tunneling current reveals unexpected properties of superconductor surfaces, including subsurface defects and surface phases, and enables injection of new kinds of defects with sub-nm resolution. The combination of these techniques paves way to comprehensive analysis of disorder across measurements and materials, and ultimately developing the “axis of disorder” as an approach to control superconducting and topological properties with near atomic-scale accuracy.