Fig. 1: Dynamic, human-in-the-loop Bayesian optimized active recommender engine (BOARS) architecture.

In this AE workflow, the step under the orange region is the contribution in this paper where we introduce a human-operator active recommender system to vote and build a target spectral through visual inspection and define a reward-based structural similarity-based objective function. The steps under green and yellow regions are traditional Bayesian optimization (BO) workflow and instrument (microscope) operations to scan an image of the sample and capture spectra at BO-guided locations over the image space. Additionally, the red highlighted arrow between the yellow and orange region is another contribution of the paper which builds the connection of the workflow between human-operated tasks (recommender system) and the microscope operations for real-time implementations of this overall human-in-the-loop AE architecture. The other red highlighted arrows signify the coupling between different environments of the framework: between microscope and traditional BO workflow and vice-versa, and between human-in-the-loop part and traditional BO workflow.