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Active learning methods are rapidly becoming the integral component ofautomated experiment workflows in imaging, materials synthesis, andcomputation. The distinctive aspect of many experimental scenarios is thepresence of multiple information channels, including both the intrinsicmodalities of the measurement system and the exogenous environment and noisesignals. One of the key tasks in experimental studies is hence establishingwhich of these channels is predictive of the behaviors of interest. Here weexplore the problem of discovery of the optimal predictive channel forstructure-property relationships (in microscopy) using deep kernel learning formodality selection in an active experiment setting. We further pose that thisapproach can be directly applicable to similar active learning tasks inautomated synthesis and the discovery of quantitative structure-activityrelations in molecular systems.

Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning