A Dynamic Bayesian Optimized Active Recommender System For Curiosity-driven Partially Human-in-the-loop Automated Experiments

This article discusses the growing trend of using active learning methods to optimize experimental materials synthesis and characterization. The authors present a human-AI collaborated workflow, using a Bayesian optimized active recommender system, to shape targets in real-time with human feedback. They showcase examples of this framework applied to pre-acquired piezoresponse force spectroscopy of a ferroelectric thin film and in real-time on an atomic force microscope. The article highlights the potential of human-AI approaches for curiosity-driven exploration of systems across experimental domains.