Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy
This work is incremental, discussing operational considerations for applying existing ML methods to enhance microscopy experiments.
The paper addresses the challenges of transitioning to real-time, closed-loop machine learning for automated experimentation in scanning transmission electron microscopy, focusing on workflow design, data handling, and human-ML collaboration.
Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centered experiment workflow design and optimization. Here, we discuss the associated challenges with the transition to active ML, including sequential data analysis and out-of-distribution drift effects, the requirements for the edge operation, local and cloud data storage, and theory in the loop operations. Specifically, we discuss the relative contributions of human scientists and ML agents in the ideation, orchestration, and execution of experimental workflows and the need to develop universal hyper languages that can apply across multiple platforms. These considerations will collectively inform the operationalization of ML in next-generation experimentation.