  The broad adoption of machine learning (ML)-based automated and autonomous
experiments (AE) in physical characterization and synthesis requires
development of strategies for understanding and intervention in the
experimental workflow. Here, we introduce and realize strategies for
post-acquisition forensic analysis applied to the deep kernel learning based AE
scanning probe microscopy. This approach yields real-time and post-acquisition
indicators of the progression of an active learning process interacting with an
experimental system. We further illustrate that this approach can be extended
towards human-in-the-loop autonomous experiments, where human operators make
high-level decisions at high latencies setting the policies for AE, and the ML
algorithm performs low-level fast decisions. The proposed approach is universal
and can be extended to other physical and chemical imaging techniques and
applications such as combinatorial library analysis. The full forensic analysis
notebook is publicly available on GitHub at
https://github.com/yongtaoliu/Forensics-DKL-BEPS.
