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Title: Building and exploring libraries of atomic defects in graphene: Scanning transmission electron and scanning tunneling microscopy study

Abstract

The presence and configurations of defects are primary components determining materials functionality. Their population and distribution are often nonergodic and dependent on synthesis history, and therefore rarely amenable to direct theoretical prediction. Here, dynamic electron beam–induced transformations in Si deposited on a graphene monolayer are used to create libraries of possible Si and carbon vacancy defects. Deep learning networks are developed for automated image analysis and recognition of the defects, creating a library of (meta) stable defect configurations. Density functional theory is used to estimate atomically resolved scanning tunneling microscopy (STM) signatures of the classified defects from the created library, allowing identification of several defect types across imaging platforms. This approach allows automatic creation of defect libraries in solids, exploring the metastable configurations always present in real materials, and correlative studies with other atomically resolved techniques, providing comprehensive insight into defect functionalities.

Authors:
ORCiD logo [1]; ORCiD logo [1];  [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  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) (SC-22). Materials Sciences & Engineering Division
OSTI Identifier:
1607267
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Science Advances
Additional Journal Information:
Journal Volume: 5; Journal Issue: 9; Journal ID: ISSN 2375-2548
Publisher:
AAAS
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

Ziatdinov, Maxim, Dyck, Ondrej, Li, Xin, Sumpter, Bobby G., Jesse, Stephen, Vasudevan, Rama K., and Kalinin, Sergei V. Building and exploring libraries of atomic defects in graphene: Scanning transmission electron and scanning tunneling microscopy study. United States: N. p., 2019. Web. https://doi.org/10.1126/sciadv.aaw8989.
Ziatdinov, Maxim, Dyck, Ondrej, Li, Xin, Sumpter, Bobby G., Jesse, Stephen, Vasudevan, Rama K., & Kalinin, Sergei V. Building and exploring libraries of atomic defects in graphene: Scanning transmission electron and scanning tunneling microscopy study. United States. https://doi.org/10.1126/sciadv.aaw8989
Ziatdinov, Maxim, Dyck, Ondrej, Li, Xin, Sumpter, Bobby G., Jesse, Stephen, Vasudevan, Rama K., and Kalinin, Sergei V. Fri . "Building and exploring libraries of atomic defects in graphene: Scanning transmission electron and scanning tunneling microscopy study". United States. https://doi.org/10.1126/sciadv.aaw8989. https://www.osti.gov/servlets/purl/1607267.
@article{osti_1607267,
title = {Building and exploring libraries of atomic defects in graphene: Scanning transmission electron and scanning tunneling microscopy study},
author = {Ziatdinov, Maxim and Dyck, Ondrej and Li, Xin and Sumpter, Bobby G. and Jesse, Stephen and Vasudevan, Rama K. and Kalinin, Sergei V.},
abstractNote = {The presence and configurations of defects are primary components determining materials functionality. Their population and distribution are often nonergodic and dependent on synthesis history, and therefore rarely amenable to direct theoretical prediction. Here, dynamic electron beam–induced transformations in Si deposited on a graphene monolayer are used to create libraries of possible Si and carbon vacancy defects. Deep learning networks are developed for automated image analysis and recognition of the defects, creating a library of (meta) stable defect configurations. Density functional theory is used to estimate atomically resolved scanning tunneling microscopy (STM) signatures of the classified defects from the created library, allowing identification of several defect types across imaging platforms. This approach allows automatic creation of defect libraries in solids, exploring the metastable configurations always present in real materials, and correlative studies with other atomically resolved techniques, providing comprehensive insight into defect functionalities.},
doi = {10.1126/sciadv.aaw8989},
journal = {Science Advances},
number = 9,
volume = 5,
place = {United States},
year = {2019},
month = {9}
}

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Works referenced in this record:

Atom-by-atom structural and chemical analysis by annular dark-field electron microscopy
journal, March 2010

  • Krivanek, Ondrej L.; Chisholm, Matthew F.; Nicolosi, Valeria
  • Nature, Vol. 464, Issue 7288
  • DOI: 10.1038/nature08879

Analysis of graphene nanoribbons as a channel material for field-effect transistors
journal, April 2006

  • Obradovic, B.; Kotlyar, R.; Heinz, F.
  • Applied Physics Letters, Vol. 88, Issue 14
  • DOI: 10.1063/1.2191420

Placing single atoms in graphene with a scanning transmission electron microscope
journal, September 2017

  • Dyck, Ondrej; Kim, Songkil; Kalinin, Sergei V.
  • Applied Physics Letters, Vol. 111, Issue 11
  • DOI: 10.1063/1.4998599

Materials Data Infrastructure: A Case Study of the Citrination Platform to Examine Data Import, Storage, and Access
journal, June 2016


Spin qubits in graphene quantum dots
journal, February 2007

  • Trauzettel, Björn; Bulaev, Denis V.; Loss, Daniel
  • Nature Physics, Vol. 3, Issue 3
  • DOI: 10.1038/nphys544

Image registration of low signal-to-noise cryo-STEM data
journal, August 2018


Theory and Application for the Scanning Tunneling Microscope
journal, June 1983


Graphene Reknits Its Holes
journal, July 2012

  • Zan, Recep; Ramasse, Quentin M.; Bangert, Ursel
  • Nano Letters, Vol. 12, Issue 8
  • DOI: 10.1021/nl300985q

Focal Loss for Dense Object Detection
conference, October 2017

  • Lin, Tsung-Yi; Goyal, Priya; Girshick, Ross
  • 2017 IEEE International Conference on Computer Vision (ICCV)
  • DOI: 10.1109/ICCV.2017.324

Van der Waals density functionals applied to solids
journal, May 2011


A Deep Learning Approach to Identify Local Structures in Atomic-Resolution Transmission Electron Microscopy Images
journal, July 2018

  • Madsen, Jacob; Liu, Pei; Kling, Jens
  • Advanced Theory and Simulations, Vol. 1, Issue 8
  • DOI: 10.1002/adts.201800037

Norm-conserving and ultrasoft pseudopotentials for first-row and transition elements
journal, October 1994


Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS2
journal, February 2019


From ultrasoft pseudopotentials to the projector augmented-wave method
journal, January 1999


Learning surface molecular structures via machine vision
journal, August 2017

  • Ziatdinov, Maxim; Maksov, Artem; Kalinin, Sergei V.
  • npj Computational Materials, Vol. 3, Issue 1
  • DOI: 10.1038/s41524-017-0038-7

Picometre-precision analysis of scanning transmission electron microscopy images of platinum nanocatalysts
journal, June 2014

  • Yankovich, Andrew B.; Berkels, Benjamin; Dahmen, W.
  • Nature Communications, Vol. 5, Issue 1
  • DOI: 10.1038/ncomms5155

Manipulating low-dimensional materials down to the level of single atoms with electron irradiation
journal, September 2017


The Atomic Circus: Small Electron Beams Spotlight Advanced Materials Down to the Atomic Scale
journal, October 2018


Transition-Metal-Atom-Embedded Graphane and Its Spintronic Device Applications
journal, October 2011

  • Da, Haixia; Feng, Yuan Ping; Liang, Gengchaiu
  • The Journal of Physical Chemistry C, Vol. 115, Issue 46
  • DOI: 10.1021/jp203506z

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
journal, December 2017

  • Badrinarayanan, Vijay; Kendall, Alex; Cipolla, Roberto
  • IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, Issue 12
  • DOI: 10.1109/TPAMI.2016.2644615

Theory of the scanning tunneling microscope
journal, January 1985


Carrier-induced antiferromagnet of graphene islands embedded in hexagonal boron nitride
journal, August 2011


Z-shaped graphene nanoribbon quantum dot device
journal, July 2007

  • Wang, Z. F.; Shi, Q. W.; Li, Qunxiang
  • Applied Physics Letters, Vol. 91, Issue 5
  • DOI: 10.1063/1.2761266

Effective Cleaning of Hexagonal Boron Nitride for Graphene Devices
journal, January 2012

  • Garcia, Andrei G. F.; Neumann, Michael; Amet, François
  • Nano Letters, Vol. 12, Issue 9
  • DOI: 10.1021/nl3011726

Mitigating e-beam-induced hydrocarbon deposition on graphene for atomic-scale scanning transmission electron microscopy studies
journal, January 2018

  • Dyck, Ondrej; Kim, Songkil; Kalinin, Sergei V.
  • Journal of Vacuum Science & Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phenomena, Vol. 36, Issue 1
  • DOI: 10.1116/1.5003034

Electron-Beam Manipulation of Silicon Dopants in Graphene
journal, June 2018


Ballistic Transport in Graphene Nanostrips in the Presence of Disorder:  Importance of Edge Effects
journal, January 2007

  • Areshkin, Denis A.; Gunlycke, Daniel; White, Carter T.
  • Nano Letters, Vol. 7, Issue 1
  • DOI: 10.1021/nl062132h

Performance Projections for Ballistic Graphene Nanoribbon Field-Effect Transistors
journal, April 2007

  • Liang, Gengchiau; Neophytou, Neophytos; Nikonov, Dmitri E.
  • IEEE Transactions on Electron Devices, Vol. 54, Issue 4
  • DOI: 10.1109/TED.2007.891872

Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies
journal, March 2017

  • Green, M. L.; Choi, C. L.; Hattrick-Simpers, J. R.
  • Applied Physics Reviews, Vol. 4, Issue 1
  • DOI: 10.1063/1.4977487

Soft self-consistent pseudopotentials in a generalized eigenvalue formalism
journal, April 1990


Engineering and modifying two-dimensional materials by electron beams
journal, September 2017

  • Zhao, Xiaoxu; Kotakoski, Jani; Meyer, Jannik C.
  • MRS Bulletin, Vol. 42, Issue 09
  • DOI: 10.1557/mrs.2017.184

Stone-Wales-type transformations in carbon nanostructures driven by electron irradiation
journal, June 2011


Addressing the isomer cataloguing problem for nanopores in two-dimensional materials
journal, January 2019


Atom-by-atom fabrication by electron beam via induced phase transformations
journal, September 2017

  • Jiang, Nan; Zarkadoula, Eva; Narang, Prineha
  • MRS Bulletin, Vol. 42, Issue 09
  • DOI: 10.1557/mrs.2017.183

Quantum dots and spin qubits in graphene
journal, July 2010


High performance current and spin diode of atomic carbon chain between transversely symmetric ribbon electrodes
journal, August 2014

  • Dong, Yao-Jun; Wang, Xue-Feng; Yang, Shuo-Wang
  • Scientific Reports, Vol. 4, Issue 1
  • DOI: 10.1038/srep06157

Machine-learning-assisted materials discovery using failed experiments
journal, May 2016

  • Raccuglia, Paul; Elbert, Katherine C.; Adler, Philip D. F.
  • Nature, Vol. 533, Issue 7601
  • DOI: 10.1038/nature17439

Direct Sub-Angstrom Imaging of a Crystal Lattice
journal, September 2004


The Materials Data Facility: Data Services to Advance Materials Science Research
journal, July 2016


Graphene quantum dots embedded in hexagonal boron nitride sheets
journal, January 2011

  • Li, Junwen; Shenoy, Vivek B.
  • Applied Physics Letters, Vol. 98, Issue 1
  • DOI: 10.1063/1.3533804

Role of materials data science and informatics in accelerated materials innovation
journal, August 2016

  • Kalidindi, Surya R.; Brough, David B.; Li, Shengyen
  • MRS Bulletin, Vol. 41, Issue 08
  • DOI: 10.1557/mrs.2016.164

Probing the Bonding and Electronic Structure of Single Atom Dopants in Graphene with Electron Energy Loss Spectroscopy
journal, January 2013

  • Ramasse, Quentin M.; Seabourne, Che R.; Kepaptsoglou, Despoina-Maria
  • Nano Letters, Vol. 13, Issue 10
  • DOI: 10.1021/nl304187e

Large scale atmospheric pressure chemical vapor deposition of graphene
journal, April 2013


    Works referencing / citing this record:

    Atomic Mechanisms for the Si Atom Dynamics in Graphene: Chemical Transformations at the Edge and in the Bulk
    journal, November 2019

    • Ziatdinov, Maxim; Dyck, Ondrej; Jesse, Stephen
    • Advanced Functional Materials, Vol. 29, Issue 52
    • DOI: 10.1002/adfm.201904480