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

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.

doi:10.1186/s40679-020-00070-x

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

Journal Article · Mon Mar 30 00:00:00 EDT 2020 · Advanced Structural and Chemical Imaging

DOI:https://doi.org/10.1186/s40679-020-00070-x· OSTI ID:1649061

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Vanderbilt Univ., Nashville, TN (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)
Vanderbilt Univ., Nashville, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Chinese Academy of Sciences (CAS), Beijing (China)

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.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1649061

Journal Information:: Advanced Structural and Chemical Imaging, Journal Name: Advanced Structural and Chemical Imaging Journal Issue: 1 Vol. 6; ISSN 2198-0926

Publisher:: BioMed CentralCopyright Statement

Country of Publication:: United States

Language:: English

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