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Title: Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning

Abstract

Using electron beam manipulation, we enable deterministic motion of individual Si atoms in graphene along predefined trajectories. Structural evolution during the dopant motion was explored, providing information on changes of the Si atom neighborhood during atomic motion and providing statistical information of possible defect configurations. The combination of a Gaussian mixture model and principal component analysis applied to the deep learning-processed experimental data allowed disentangling of the atomic distortions for two different graphene sublattices. This approach demonstrates the potential of e-beam manipulation to create defect libraries of multiple realizations of the same defect and explore the potential of symmetry breaking physics. The rapid image analytics enabled via a deep learning network further empowers instrumentation for e-beam controlled atom-by-atom fabrication. Here, the analysis described in the paper can be reproduced via an interactive Jupyter notebook at https://git.io/JJ3Bx.

Authors:
ORCiD logo [1];  [1];  [1];  [1]; ORCiD logo [1]
+ Show Author Affiliations
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
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). Materials Sciences & Engineering Division; USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1785638
Alternate Identifier(s):
OSTI ID: 1682361
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Nanotechnology
Additional Journal Information:
Journal Volume: 32; Journal Issue: 3; Journal ID: ISSN 0957-4484
Publisher:
IOP Publishing
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; 77 NANOSCIENCE AND NANOTECHNOLOGY

Citation Formats

Maxim, Ziatdinov, Jesse, Stephen, Sumpter, Bobby G., Kalinin, Sergei V., and Dyck, Ondrej. Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning. United States: N. p., 2020. Web. doi:10.1088/1361-6528/abb8a6.
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Maxim, Ziatdinov, Jesse, Stephen, Sumpter, Bobby G., Kalinin, Sergei V., & Dyck, Ondrej. Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning. United States. https://doi.org/10.1088/1361-6528/abb8a6
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Maxim, Ziatdinov, Jesse, Stephen, Sumpter, Bobby G., Kalinin, Sergei V., and Dyck, Ondrej. Wed . "Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning". United States. https://doi.org/10.1088/1361-6528/abb8a6. https://www.osti.gov/servlets/purl/1785638.
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@article{osti_1785638,
title = {Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning},
author = {Maxim, Ziatdinov and Jesse, Stephen and Sumpter, Bobby G. and Kalinin, Sergei V. and Dyck, Ondrej},
abstractNote = {Using electron beam manipulation, we enable deterministic motion of individual Si atoms in graphene along predefined trajectories. Structural evolution during the dopant motion was explored, providing information on changes of the Si atom neighborhood during atomic motion and providing statistical information of possible defect configurations. The combination of a Gaussian mixture model and principal component analysis applied to the deep learning-processed experimental data allowed disentangling of the atomic distortions for two different graphene sublattices. This approach demonstrates the potential of e-beam manipulation to create defect libraries of multiple realizations of the same defect and explore the potential of symmetry breaking physics. The rapid image analytics enabled via a deep learning network further empowers instrumentation for e-beam controlled atom-by-atom fabrication. Here, the analysis described in the paper can be reproduced via an interactive Jupyter notebook at https://git.io/JJ3Bx.},
doi = {10.1088/1361-6528/abb8a6},
journal = {Nanotechnology},
number = 3,
volume = 32,
place = {United States},
year = {Wed Oct 21 00:00:00 EDT 2020},
month = {Wed Oct 21 00:00:00 EDT 2020}
}
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Journal Article:
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Publisher's Version of Record
https://doi.org/10.1088/1361-6528/abb8a6
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