An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics
This work addresses the problem of impractical automation in microscopy for materials science researchers, representing an incremental advancement by integrating existing methods for improved instrument control.
The researchers tackled the challenge of automating scanning transmission electron microscopy (STEM) by developing a closed-loop control platform guided by sparse data analytics, which enables automated analysis of material features and facilitates high-throughput studies.
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.