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Title: Identifying local structural states in atomic imaging by computer vision

Abstract

Abstract The availability of atomically resolved imaging modalities enables an unprecedented view into the local structural states of materials, which manifest themselves by deviations from the fundamental assumptions of periodicity and symmetry. Consequently, approaches that aim to extract these local structural states from atomic imaging data with minimal assumptions regarding the average crystallographic configuration of a material are indispensable to advances in structural and chemical investigations of materials. Here, we present an approach to identify and classify local structural states that is rooted in computer vision. This approach introduces a definition of a structural state that is composed of both local and nonlocal information extracted from atomically resolved images, and is wholly untethered from the familiar concepts of symmetry and periodicity. Instead, this approach relies on computer vision techniques such as feature detection, and concepts such as scale invariance. We present the fundamental aspects of local structural state extraction and classification by application to simulated scanning transmission electron microscopy images, and analyze the robustness of this approach in the presence of common instrumental factors such as noise, limited spatial resolution, and weak contrast. Finally, we apply this computer vision-based approach for the unsupervised detection and classification of local structural statesmore » in an experimental electron micrograph of a complex oxides interface, and a scanning tunneling micrograph of a defect-engineered multilayer graphene surface.« less

Authors:
ORCiD logo; ; ;
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1330726
Alternate Identifier(s):
OSTI ID: 1333065
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Published Article
Journal Name:
Advanced Structural and Chemical Imaging
Additional Journal Information:
Journal Name: Advanced Structural and Chemical Imaging Journal Volume: 2 Journal Issue: 1; Journal ID: ISSN 2198-0926
Publisher:
Springer Science + Business Media
Country of Publication:
Germany
Language:
English
Subject:
74 ATOMIC AND MOLECULAR PHYSICS

Citation Formats

Laanait, Nouamane, Ziatdinov, Maxim, He, Qian, and Borisevich, Albina. Identifying local structural states in atomic imaging by computer vision. Germany: N. p., 2016. Web. doi:10.1186/s40679-016-0028-8.
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Laanait, Nouamane, Ziatdinov, Maxim, He, Qian, & Borisevich, Albina. Identifying local structural states in atomic imaging by computer vision. Germany. https://doi.org/10.1186/s40679-016-0028-8
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Laanait, Nouamane, Ziatdinov, Maxim, He, Qian, and Borisevich, Albina. Wed . "Identifying local structural states in atomic imaging by computer vision". Germany. https://doi.org/10.1186/s40679-016-0028-8.
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@article{osti_1330726,
title = {Identifying local structural states in atomic imaging by computer vision},
author = {Laanait, Nouamane and Ziatdinov, Maxim and He, Qian and Borisevich, Albina},
abstractNote = {Abstract The availability of atomically resolved imaging modalities enables an unprecedented view into the local structural states of materials, which manifest themselves by deviations from the fundamental assumptions of periodicity and symmetry. Consequently, approaches that aim to extract these local structural states from atomic imaging data with minimal assumptions regarding the average crystallographic configuration of a material are indispensable to advances in structural and chemical investigations of materials. Here, we present an approach to identify and classify local structural states that is rooted in computer vision. This approach introduces a definition of a structural state that is composed of both local and nonlocal information extracted from atomically resolved images, and is wholly untethered from the familiar concepts of symmetry and periodicity. Instead, this approach relies on computer vision techniques such as feature detection, and concepts such as scale invariance. We present the fundamental aspects of local structural state extraction and classification by application to simulated scanning transmission electron microscopy images, and analyze the robustness of this approach in the presence of common instrumental factors such as noise, limited spatial resolution, and weak contrast. Finally, we apply this computer vision-based approach for the unsupervised detection and classification of local structural states in an experimental electron micrograph of a complex oxides interface, and a scanning tunneling micrograph of a defect-engineered multilayer graphene surface.},
doi = {10.1186/s40679-016-0028-8},
journal = {Advanced Structural and Chemical Imaging},
number = 1,
volume = 2,
place = {Germany},
year = {Wed Nov 02 00:00:00 EDT 2016},
month = {Wed Nov 02 00:00:00 EDT 2016}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1186/s40679-016-0028-8
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Figures / Tables:

Fig. 1 Fig. 1: Structural states as scale-invariant features. a Simulated STEM images of bulk SrTiO3 and SrTiO3/BaTiO3 interface with the electron beam propagating along the [100] crystallographic direction. Images are convoluted with a Gaussian function with FWHM of 0.7 Å to account for the finite source size of the electron beam.more » b Features extracted by the Laplacian of Gaussian detector are shown as an overlay of circles on the images in a. The intensity scale was inverted to improve the visibility. The size of the circle indicates the scale at which the feature was detected. For simplicity in the ensuing analyses, the contrast threshold of the LoG is tuned so that oxygen columns in the right image in b are not detected (see Additional file 1 for all atomic columns). c Close-up of the left image in b indicating both the keypoint, Kp, which describes the atom locally and the descriptor vectors, Ds, which encode the intensity distribution of neighboring columns to provide a nonlocal description of the column. Descriptors for the different atomic columns are shown as 1-dimensional vectors, indicating that columns with the same intensity can have different descriptors due to the different angular configuration of their neighboring atoms. The structural state, in this case an atomic column, is then defined by the pair composed of Kp, Ds). The implementations of the LoG detector in the Python scikit-image library [41] and SIFT in OpenCV [42] were used throughout« less
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