  Artificial intelligence (AI) promises to reshape scientific inquiry and
enable breakthrough discoveries in areas such as energy storage, quantum
computing, and biomedicine. Scanning transmission electron microscopy (STEM), a
cornerstone of the study of chemical and materials systems, stands to benefit
greatly from AI-driven automation. However, present barriers to low-level
instrument control, as well as generalizable and interpretable feature
detection, make truly automated microscopy impractical. Here, we discuss the
design of a closed-loop instrument control platform guided by emerging sparse
data analytics. We demonstrate how a centralized controller, informed by
machine learning combining limited $a$ $priori$ knowledge and task-based
discrimination, can drive on-the-fly experimental decision-making. This
platform unlocks practical, automated analysis of a variety of material
features, enabling new high-throughput and statistical studies.

Materials Science;Machine Learning