  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 a 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, which
autonomously guides 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. In combination with the flexible
scalarizer function that allows to ascribe the degree of physical interest to
predicted spectra, this enables physical discovery in automated experiment.
Here, this approach is illustrated for nanoplasmonic studies of nanoparticles
and experimentally implemented in a truly autonomous fashion for bulk- and edge
plasmon discovery in MnPS3, a lesser-known beam-sensitive layered 2D material.
This approach is universal, can be directly used as-is with any specimen, and
is expected to be applicable to any probe-based microscopic techniques
including other STEM modalities, Scanning Probe Microscopies, chemical, and
optical imaging.
