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DOE OSTI · 1873446

An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics

Olszta, Matthew (ORCID:0000000233437810)·Hopkins, Derek·Fiedler, Kevin R. (ORCID:0000000296567663)·Oostrom, Marjolein (ORCID:0000000212969084)·Akers, Sarah (ORCID:0000000337275801)·Spurgeon, Steven R. (ORCID:000000031218839X)
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Abstract

Abstract 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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47 OTHER INSTRUMENTATIONautomationhigh-throughputmachine learningscanning transmission electron microscopysparse data analytics

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BibTeXRIS

Olszta, Matthew (ORCID:0000000233437810), Hopkins, Derek, Fiedler, Kevin R. (ORCID:0000000296567663), Oostrom, Marjolein (ORCID:0000000212969084), Akers, Sarah (ORCID:0000000337275801), Spurgeon, Steven R. (ORCID:000000031218839X). 2022-10-01. An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics. https://doi.org/10.1017/s1431927622012065

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