Active Learning-Driven Automated Scanning Probe Microscopy Enables Discovery of Structure-Property Relationship

The functionalities of topological and structural defects in ferroelectric materials including domain wall dynamics, conductivity of topological defects, and light-induced phenomena have been a source of much fascination in the condensed matter physics community. Until now, the search for interesting functionalities has been guided by auxiliary information from scanning probe microscopy (SPM) to identify potential objects of interest based on human intuition. Here, we developed a machine learning-based automated SPM workflow that actively discovers relationships between domain structure and functional responses (hysteresis loops, I-V curves, non-linearities). This automated workflow combines the power of machine learning methods to learn the correlative relationships between high dimensional data, and human-based physics insights encoded in the acquisition function. This approach demonstrates that the discovery path and sampling points of on-field and off-field hysteresis loops are largely different in a PbTiO₃ thin film. The larger polarization mobility in the vicinity of 180^o walls results in more significant hysteresis loop opening in on-field measurements, while off-field measurements detect only the slowly relaxing components due to stronger pinning at the ferroelastic walls. This approach can be adapted to apply to a broad range of imaging and spectroscopy methods, e.g., SPM, electron microscopy, optical microscopy, and chemical imaging.