# An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics

arXiv: https://arxiv.org/abs/2109.14772 · HTML: https://mlsanity.com/paper/2109.14772/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 81 references that are in the map.
- First posted on arXiv 30 Sep 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Low Data Drug Discovery with One-shot Learning](https://mlsanity.com/paper/1611.03199/) (2016)
- [Review: Deep Learning in Electron Microscopy](https://mlsanity.com/paper/2009.08328/) (2020)
- [AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy Data in (Scanning) Transmission Electron Microscopy and Beyond](https://mlsanity.com/paper/2105.07485/) (2021)
- [ImageJ2: ImageJ for the next generation of scientific image data](https://mlsanity.com/paper/1701.05940/) (2017)
- [Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data](https://mlsanity.com/paper/2107.10387/) (2021)
- [Machine learning with limited data](https://mlsanity.com/paper/2101.11461/) (2021)

## Built on by (most cited first, 7 of 7)

- [Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images](https://mlsanity.com/paper/2301.07743/) (2023)
- [Towards Augmented Microscopy with Reinforcement Learning-Enhanced Workflows](https://mlsanity.com/paper/2208.02865/) (2022)
- [Unsupervised segmentation of irradiationunicodex2010induced orderunicodex2010disorder phase transitions in electron microscopy](https://mlsanity.com/paper/2311.08585/) (2023)
- [Revealing the Evolution of Order in Materials Microstructures Using Multi-Modal Computer Vision](https://mlsanity.com/paper/2411.09896/) (2024)
- [Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes](https://mlsanity.com/paper/2511.08350/) (2025)
- [Deep-learning-based prediction of nanoparticle phase transitions during in situ transmission electron microscopy](https://mlsanity.com/paper/2205.11407/) (2022)
- [An Interactive, Automated 4D-STEM data acquisition and analysis routine for Scanning Electron Nanobeam Diffraction and Ptychography experiments](https://mlsanity.com/paper/2608.13752/) (2026)

## 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.