A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments
This work addresses the need for more interactive and curiosity-driven exploration in experimental materials science, though it appears incremental as it builds on existing active learning methods by incorporating human feedback.
The authors tackled the problem of limited human feedback in experimental optimization by developing a Bayesian optimized active recommender system (BOARS) that uses human feedback to shape targets on the fly, showcasing its application to piezoresponse force spectroscopy and achieving optimization of symmetric piezoresponse amplitude hysteresis loops in real-time on an atomic force microscope.
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