  We develop and implement an automated experiment in multimodal imaging to
probe structural, chemical, and functional behaviors in complex materials and
elucidate the dominant physical mechanisms that control device function. Here
the emergence of non-linear electromechanical responses in piezoresponse force
microscopy (PFM) is explored. Non-linear responses in PFM can originate from
multiple mechanisms, including intrinsic material responses often controlled by
domain structure, surface topography that affects the mechanical phenomena at
the tip-surface junction, and, potentially, the presence of surface
contaminants. Using an automated experiment to probe the origins of non-linear
behavior in model ferroelectric lead titanate (PTO) and ferroelectric
Al0.93B0.07N films, it was found that PTO showed asymmetric nonlinear behavior
across a/c domain walls and a broadened high nonlinear response region around
c/c domain walls. In contrast, for Al0.93B0.07N, well-poled regions showed high
linear piezoelectric responses paired with low non-linear responses and regions
that were multidomain indicated low linear responses and high nonlinear
responses. We show that formulating dissimilar exploration strategies in deep
kernel learning as alternative hypotheses allows for establishing the
preponderant physical mechanisms behind the non-linear behaviors, suggesting
that this approach automated experiments can potentially discern between
competing physical mechanisms. This technique can also be extended to electron,
probe, and chemical imaging.
