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Optimization of experimental materials synthesis and characterization throughactive learning methods has been growing over the last decade, with examplesranging from measurements of diffraction on combinatorial alloys atsynchrotrons, to searches through chemical space with automated synthesisrobots for perovskites. In virtually all cases, the target property of interestfor optimization is defined apriori with limited human feedback duringoperation. In contrast, here we present the development of a new type of humanin the loop experimental workflow, via a Bayesian optimized active recommendersystem (BOARS), to shape targets on the fly, employing human feedback. Weshowcase examples of this framework applied to pre-acquired piezoresponse forcespectroscopy of a ferroelectric thin film, and then implement this in real timeon an atomic force microscope, where the optimization proceeds to findsymmetric piezoresponse amplitude hysteresis loops. It is found that suchfeatures appear more affected by subsurface defects than the local domainstructure. This work shows the utility of human-augmented machine learningapproaches for curiosity-driven exploration of systems across experimentaldomains. The analysis reported here is summarized in Colab Notebook for thepurpose of tutorial and application to other data:https://github.com/arpanbiswas52/varTBO

A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments