Abstract
AbstractMachine 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-centric 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 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, as well as 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.
Funder
U.S. Department of Energy
U.S. Department of Commerce
United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Laboratory
National Science Foundation
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Mechanics of Materials,General Materials Science,Modeling and Simulation
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