  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.
