Deep learning pipeline for statistical quantification of amorphous two-dimensional materials

13 October 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Aberration-corrected transmission electron microscopy enables imaging of two-dimensional (2D) materials with atomic resolution. However, dissecting the short-range-ordered structures in radiation-sensitive and amorphous 2D materials remains a significant challenge due to low atomic contrast and laborious manual evaluation. Here, we imaged carbon-based 2D materials with strong contrast, which is enabled by chromatic and spherical aberration correction at low acceleration voltage. By constructing a deep learning pipeline, atomic registry in amorphous 2D materials can be precisely determined, providing access to a full spectrum of quantitative datasets, including bond length/angle distribution, pair distribution function, and real-space polygon mapping. Accurate segmentation of micropores and surface contamination, together with robustness against background inhomogeneity, guaranteed the quantification validity in complex experimental images. The automated image analysis provides quantitative metrics with high efficiency and throughput, which may bring new insights into the structural understanding of short-range-ordered structures. In addition, the convolutional neural network can be readily generalized to crystalline materials, allowing for automatic defect identification and strain mapping.


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

Supplementary materials

Supporting information
Comparison between automated analysis and hand-labeling, including identification of atomic position, segmentation of monolayer region. Additional demonstration of neural network capabilities, including area-dependent statistic, PDF analysis, etc. Simulation parameters and listing of CNN parameters.


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