Novelty-Driven Target-Space Discovery in Automated Electron and Scanning Probe Microscopy
This paper introduces BEACON, a deep-kernel-learning framework that enables automated electron and scanning probe microscopy to actively discover novel scientific behaviors in target spaces by learning structure-property relationships during experiments, a method validated through rigorous offline benchmarking and successfully deployed on a scanning transmission electron microscope (STEM).