  Microscopy techniques have played vital roles in materials science, biology,
and nanotechnology, offering high-resolution imaging and detailed insights into
properties at nanoscale and atomic level. The automation of microscopy
experiments, in combination with machine learning approaches, is a
transformative advancement, offering increased efficiency, reproducibility, and
the capability to perform complex experiments. Our previous work on autonomous
experimentation with scanning probe microscopy (SPM) demonstrated an active
learning framework using deep kernel learning (DKL) for structure-property
relationship discovery. This approach has demonstrated broad applications in
various microscopy techniques. Here, to address limitations of workflows based
on DKL, we developed methods to incorporate prior knowledge and human interest
into DKL-based workflows and implemented these workflows in SPM. By integrating
expected rewards from structure libraries or spectroscopic features, we
enhanced the exploration efficiency of autonomous microscopy, demonstrating
more efficient and targeted exploration in autonomous microscopy. We
demonstrated the application of these methods in SPM, but we suggest that these
methods can be seamlessly applied to other microscopy and imaging techniques.
Furthermore, the concept can be adapted for general Bayesian optimization in
material discovery across a broad range of autonomous experimental fields.
