An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics

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 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

References in corpus (6)

• Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations
• Physics discovery in nanoplasmonic systems via autonomous experiments in Scanning Transmission Electron Microscopy
• Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data
• Low Data Drug Discovery with One-shot Learning
• Machine learning with limited data
• Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy

Cited by in corpus (7)

• Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy
• Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images
• Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data
• Towards Augmented Microscopy with Reinforcement Learning-Enhanced Workflows
• Evaluating Stage Motion for Automated Electron Microscopy
• Mind the Gap: Bridging the Divide Between AI Aspirations and the Reality of Autonomous Characterization
• Revealing the Evolution of Order in Materials Microstructures Using Multi-Modal Computer Vision