Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics to high dielectric and electromechanical responses. Many of these have been discovered and quantified via local scanning probe microscopy methods. However, the search for these functionalities has until now been based by either trial and error or using auxiliary information such as topography or domain wall structure to identify potential objects of interest based on the intuition of operator or preexisting hypotheses, with subsequent manual exploration. Here, we report the development and implementation of a machine learning framework that actively discovers relationships between local domain structure and polarization switching characteristics in ferroelectric materials encoded in the hysteresis loop. The latter and descriptors such as nucleation bias, coercive bias, hysteresis loop area, or more complex functionals of hysteresis loop shape and corresponding uncertainties are used to guide the discovery via automated piezoresponse force microscopy (PFM) and spectroscopy experiments. As such, this approach 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. For ferroelectric, this automated workflow demonstrates that the discovery path and sampling points of on-field and off-field hysteresis loops are largely different, indicating the on-field and off-field hysteresis loops are dominated by different mechanisms. The proposed approach is universal and can be applied to a broad range of modern imaging and spectroscopy methods ranging from other scanning probe microscopy modalities to electron microscopy and chemical imaging.
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