Physics-driven discovery in an autonomous experiment has emerged as a dream application of machine learning in physical sciences. Here we develop and experimentally implement deep kernel learning workflow combining the correlative prediction of the target functional response and its uncertainty from the structure, and physics-based selection of acquisition function guiding the navigation of the image space. Compared to classical Bayesian optimization methods, this approach allows to capture the complex spatial features present in the images of realistic materials, and dynamically learn structure-property relationships towards physical discovery. Here, this approach is illustrated for nanoplasmonic studies of the nanoparticles and experimentally implemented for bulk- and edge plasmon discovery in MnPS3, a lesser-known beam-sensitive layered 2D material. This approach is universal and is expected to be applicable to probe-based microscopic techniques including other STEM modalities and Scanning Probe Microscopies.
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