  Optimization of experimental materials synthesis and characterization through
active learning methods has been growing over the last decade, with examples
ranging from measurements of diffraction on combinatorial alloys at
synchrotrons, to searches through chemical space with automated synthesis
robots for perovskites. In virtually all cases, the target property of interest
for optimization is defined apriori with limited human feedback during
operation. In contrast, here we present the development of a new type of human
in the loop experimental workflow, via a Bayesian optimized active recommender
system (BOARS), to shape targets on the fly, employing human feedback. We
showcase examples of this framework applied to pre-acquired piezoresponse force
spectroscopy of a ferroelectric thin film, and then implement this in real time
on an atomic force microscope, where the optimization proceeds to find
symmetric piezoresponse amplitude hysteresis loops. It is found that such
features appear more affected by subsurface defects than the local domain
structure. This work shows the utility of human-augmented machine learning
approaches for curiosity-driven exploration of systems across experimental
domains. The analysis reported here is summarized in Colab Notebook for the
purpose of tutorial and application to other data:
https://github.com/arpanbiswas52/varTBO
