  Exploring the structural, chemical, and physical properties of matter on the
nano- and atomic scales has become possible with the recent advances in
aberration-corrected electron energy-loss spectroscopy (EELS) in scanning
transmission electron microscopy (STEM). However, the current paradigm of
STEM-EELS relies on the classical rectangular grid sampling, in which all
surface regions are assumed to be of equal a priori interest. This is typically
not the case for real-world scenarios, where phenomena of interest are
concentrated in a small number of spatial locations. One of foundational
problems is the discovery of nanometer- or atomic scale structures having
specific signatures in EELS spectra. Here we systematically explore the
hyperparameters controlling deep kernel learning (DKL) discovery workflows for
STEM-EELS and identify the role of the local structural descriptors and
acquisition functions on the experiment progression. In agreement with actual
experiment, we observe that for certain parameter combinations the experiment
path can be trapped in the local minima. We demonstrate the approaches for
monitoring automated experiment in the real and feature space of the system and
monitor knowledge acquisition of the DKL model. Based on these, we construct
intervention strategies, thus defining human-in the loop automated experiment
(hAE). This approach can be further extended to other techniques including 4D
STEM and other forms of spectroscopic imaging.
