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Title: Detection of defects in atomic-resolution images of materials using cycle analysis

Abstract

The automated detection of defects in high-angle annular dark-field Z-contrast (HAADF) scanning-transmission-electron microscopy (STEM) images has been a major challenge. Here, we report an approach for the automated detection and categorization of structural defects based on changes in the material’s local atomic geometry. The approach applies geometric graph theory to the already-found positions of atomic-column centers and is capable of detecting and categorizing any defect in thin diperiodic structures (i.e., “2D materials”) and a large subset of defects in thick diperiodic structures (i.e., 3D or bulk-like materials). Despite the somewhat limited applicability of the approach in detecting and categorizing defects in thicker bulk-like materials, it provides potentially informative insights into the presence of defects. The categorization of defects can be used to screen large quantities of data and to provide statistical data about the distribution of defects within a material. This methodology is applicable to atomic column locations extracted from any type of high-resolution image, but here we demonstrate it for HAADF STEM images.

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [4]; ORCiD logo [4]; ORCiD logo [4]; ORCiD logo [4]; ORCiD logo [5]; ORCiD logo [3]; ORCiD logo [4]; ORCiD logo [1]
  1. Vanderbilt Univ., Nashville, TN (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)
  2. Vanderbilt Univ., Nashville, TN (United States)
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)
  4. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  5. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Chinese Academy of Sciences (CAS), Beijing (China)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1649061
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Advanced Structural and Chemical Imaging
Additional Journal Information:
Journal Volume: 6; Journal Issue: 1; Journal ID: ISSN 2198-0926
Publisher:
BioMed Central
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

Ovchinnikov, Oleg S., O’Hara, Andrew, Jesse, Stephen, Hudak, Bethany M., Yang, Shi‐Ze, Lupini, Andrew R., Chisholm, Matthew F., Zhou, Wu, Kalinin, Sergei V., Borisevich, Albina Y., and Pantelides, Sokrates T. Detection of defects in atomic-resolution images of materials using cycle analysis. United States: N. p., 2020. Web. https://doi.org/10.1186/s40679-020-00070-x.
Ovchinnikov, Oleg S., O’Hara, Andrew, Jesse, Stephen, Hudak, Bethany M., Yang, Shi‐Ze, Lupini, Andrew R., Chisholm, Matthew F., Zhou, Wu, Kalinin, Sergei V., Borisevich, Albina Y., & Pantelides, Sokrates T. Detection of defects in atomic-resolution images of materials using cycle analysis. United States. https://doi.org/10.1186/s40679-020-00070-x
Ovchinnikov, Oleg S., O’Hara, Andrew, Jesse, Stephen, Hudak, Bethany M., Yang, Shi‐Ze, Lupini, Andrew R., Chisholm, Matthew F., Zhou, Wu, Kalinin, Sergei V., Borisevich, Albina Y., and Pantelides, Sokrates T. Mon . "Detection of defects in atomic-resolution images of materials using cycle analysis". United States. https://doi.org/10.1186/s40679-020-00070-x. https://www.osti.gov/servlets/purl/1649061.
@article{osti_1649061,
title = {Detection of defects in atomic-resolution images of materials using cycle analysis},
author = {Ovchinnikov, Oleg S. and O’Hara, Andrew and Jesse, Stephen and Hudak, Bethany M. and Yang, Shi‐Ze and Lupini, Andrew R. and Chisholm, Matthew F. and Zhou, Wu and Kalinin, Sergei V. and Borisevich, Albina Y. and Pantelides, Sokrates T.},
abstractNote = {The automated detection of defects in high-angle annular dark-field Z-contrast (HAADF) scanning-transmission-electron microscopy (STEM) images has been a major challenge. Here, we report an approach for the automated detection and categorization of structural defects based on changes in the material’s local atomic geometry. The approach applies geometric graph theory to the already-found positions of atomic-column centers and is capable of detecting and categorizing any defect in thin diperiodic structures (i.e., “2D materials”) and a large subset of defects in thick diperiodic structures (i.e., 3D or bulk-like materials). Despite the somewhat limited applicability of the approach in detecting and categorizing defects in thicker bulk-like materials, it provides potentially informative insights into the presence of defects. The categorization of defects can be used to screen large quantities of data and to provide statistical data about the distribution of defects within a material. This methodology is applicable to atomic column locations extracted from any type of high-resolution image, but here we demonstrate it for HAADF STEM images.},
doi = {10.1186/s40679-020-00070-x},
journal = {Advanced Structural and Chemical Imaging},
number = 1,
volume = 6,
place = {United States},
year = {2020},
month = {3}
}

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