An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics. (October 2022)

• Record Type: Journal Article
• Authors: Olszta, Matthew; Hopkins, Derek; Fiedler, Kevin R.; Oostrom, Marjolein; Akers, Sarah; Spurgeon, Steven R.
• Is Part Of: Microscopy and microanalysis. Volume 28:Number 5(2022)
• Page Start: 1611
• Page End: 1621
• Publication Date: 2022-10
• Subjects: automation -- high-throughput -- machine learning -- scanning transmission electron microscopy -- sparse data analytics; Microscopy -- Periodicals; Microchemistry -- Periodicals; 502.82
• DOI: 10.1017/S1431927622012065
• Languages: English
• ISSNs: 1431-9276

Abstract

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.