An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics
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Abstract (arXiv)
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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 8 of its 81 references that are in the map. First posted on arXiv 30 Sep 2021.
Builds on
8 of 81 references.
References · 8
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- 2020Review: Deep Learning in Electron Microscopycited by 20
- 2021AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy Data in (Scanning) Transmission Electron Microscopy and Beyondcited by 19
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Built on by
Citing papers per year: 2022: 2; 2023: 2; 2024: 1; 2025: 1; 2026: 1.
7 papers.
- 2023Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy imagescited by 8
- 2022Towards Augmented Microscopy with Reinforcement Learning-Enhanced Workflowscited by 2
- 2023Unsupervised segmentation of irradiationunicodex2010induced orderunicodex2010disorder phase transitions in electron microscopycited by 1
- 2024Revealing the Evolution of Order in Materials Microstructures Using Multi-Modal Computer Visioncited by 1
- 2025Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenescited by 1
- 2022Deep-learning-based prediction of nanoparticle phase transitions during in situ transmission electron microscopycited by 0
- 2026An Interactive, Automated 4D-STEM data acquisition and analysis routine for Scanning Electron Nanobeam Diffraction and Ptychography experimentscited by 0
- 2026An Interactive, Automated 4D-STEM data acquisition and analysis routine for Scanning Electron Nanobeam Diffraction and Ptychography experiments13 Aug 2026
- 2025Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes11 Nov 2025
- 2024Revealing the Evolution of Order in Materials Microstructures Using Multi-Modal Computer Vision15 Nov 2024
- 2023Unsupervised segmentation of irradiationunicodex2010induced orderunicodex2010disorder phase transitions in electron microscopy14 Nov 2023
- 2023Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images18 Jan 2023
- 2022Towards Augmented Microscopy with Reinforcement Learning-Enhanced Workflows4 Aug 2022
- 2022Deep-learning-based prediction of nanoparticle phase transitions during in situ transmission electron microscopy23 May 2022