An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics

Autor:	Matthew Olszta, Derek Hopkins, Kevin R Fiedler, Marjolein Oostrom, Sarah Akers, Steven R Spurgeon
Rok vydání:	2022
Předmět:	FOS: Computer and information sciences Condensed Matter - Materials Science Computer Science - Machine Learning Materials Science (cond-mat.mtrl-sci) FOS: Physical sciences Instrumentation Machine Learning (cs.LG)
Zdroj:	Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada.
ISSN:	1435-8115
Popis:	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 demonstrate how a centralized controller, informed by machine learning combining limited $a$ $priori$ knowledge and task-based discrimination, can drive on-the-fly experimental decision-making. This platform unlocks practical, automated analysis of a variety of material features, enabling new high-throughput and statistical studies. 28 pages, 3 figures
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2444be71c4ab0711284f0a998e00455f https://pubmed.ncbi.nlm.nih.gov/35686442 Zobrazit plný text záznamu