Journal
MICROSCOPY AND MICROANALYSIS
Volume 28, Issue 5, Pages 1611-1621Publisher
OXFORD UNIV PRESS
DOI: 10.1017/S1431927622012065
Keywords
automation; high-throughput; machine learning; scanning transmission electron microscopy; sparse data analytics
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Funding
- I3T Commercialization Laboratory Directed Research and Development (LDRD) program at Pacific Northwest National Laboratory (PNNL)
- U.S. Department of Energy (DOE) [DE-AC05-76RL0-1830]
- Chemical Dynamics Initiative (CDi) LDRD program
- Department of Energy's Office of Biological and Environmental Research
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This article discusses the design of a closed-loop instrument control platform guided by emerging sparse data analytics. It proposes that a centralized controller informed by machine learning can drive on-the-fly experimental decision-making, enabling practical analysis of various material features using automated microscopy.
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 hypothesize that a centralized controller, informed by machine learning combining limited a priori knowledge and task-based discrimination, could drive on-the-fly experimental decision-making. This platform may unlock practical, automated analysis of a variety of material features, enabling new high-throughput and statistical studies.
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