Identifying local structural states in atomic imaging by computer vision

Laanait, Nouamane; Ziatdinov, Maxim; He, Qian; Borisevich, Albina

doi:10.1186/s40679-016-0028-8

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

Journal Article · Wed Nov 02 00:00:00 EDT 2016 · Advanced Structural and Chemical Imaging

DOI:https://doi.org/10.1186/s40679-016-0028-8· OSTI ID:1330726

; Ziatdinov, Maxim; He, Qian; Borisevich, Albina

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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)

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

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1330726

Alternate ID(s):: OSTI ID: 1333065

Journal Information:: Advanced Structural and Chemical Imaging, Journal Name: Advanced Structural and Chemical Imaging Vol. 2 Journal Issue: 1; ISSN 2198-0926

Publisher:: Springer Science + Business MediaCopyright Statement

Country of Publication:: Germany

Language:: English

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