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Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images

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Deep-Learning Pipeline for Statistical Quantification of Amorphous Two-Dimensional Materials.

Christopher Leist¹, Meng He², Xue Liu³

¹Central Facility for Electron Microscopy, Materials Science Electron Microscopy, Universität Ulm, 89081Ulm, Germany.

|December 9, 2022

Summary

This summary is machine-generated.

We developed a deep-learning method for analyzing atomic structures in amorphous 2D materials using electron microscopy. This approach precisely maps atomic arrangements, providing quantitative insights into short-range order and defects.

Keywords:

amorphous materials automated image evaluation deep learning neural networks transmission electron microscopy two-dimensional materials

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Area of Science:

Materials Science
Nanotechnology
Electron Microscopy

Background:

Aberration-corrected transmission electron microscopy (TEM) offers atomic resolution for 2D materials.
Analyzing short-range order in amorphous and radiation-sensitive 2D materials is challenging due to low contrast and manual evaluation.

Purpose of the Study:

To develop an automated deep-learning pipeline for precise atomic structure analysis in amorphous 2D materials.
To enable quantitative characterization of short-range-ordered structures and defects.

Main Methods:

Utilized aberration-corrected TEM with low acceleration voltage for enhanced contrast imaging of carbon-based 2D materials.
Constructed a deep-learning pipeline for atomic registry determination and quantitative data extraction.
Implemented accurate segmentation for micropores and contamination, ensuring robustness against image inhomogeneity.

Main Results:

Achieved precise atomic registry determination in amorphous 2D materials.
Generated quantitative datasets including bond length/angle distributions, pair distribution functions, and polygon mapping.
Demonstrated accurate segmentation of features and robustness against experimental image complexities.
Showcased the convolutional neural network's generalizability for defect identification and strain mapping in crystalline materials.

Conclusions:

The deep-learning pipeline provides efficient and high-throughput quantitative analysis of short-range-ordered structures in amorphous 2D materials.
This automated approach enhances structural understanding and has potential applications in crystalline material analysis.