  We report the development and experimental implementation of the automated
experiment workflows for the identification of the best predictive channel for
a phenomenon of interest in spectroscopic measurements. The approach is based
on the combination of ensembled deep kernel learning for probabilistic
predictions and a basic reinforcement learning policy for channel selection. It
allows the identification of which of the available observational channels,
sampled sequentially, are most predictive of selected behaviors, and hence have
the strongest correlations. We implement this approach for multimodal imaging
in Piezoresponse Force Microscopy (PFM), with the behaviors of interest
manifesting in piezoresponse spectroscopy. We illustrate the best predictive
channel for polarization-voltage hysteresis loop and frequency-voltage
hysteresis loop areas is amplitude in the model samples. The same workflow and
code are universal and applicable for any multimodal imaging and local
characterization methods.
