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

Ziatdinov, Maxim; Dyck, Ondrej; Jesse, Stephen; Kalinin, Sergei V.

doi:10.1002/adfm.201904480

Title: Atomic Mechanisms for the Si Atom Dynamics in Graphene: Chemical Transformations at the Edge and in the Bulk

Journal Article · 22 November 2019 · Advanced Functional Materials

DOI: https://doi.org/10.1002/adfm.201904480 · OSTI ID:1607241

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Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS), and Computational Sciences and Engineering Division
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS)

The dynamic behavior of e-beam irradiated Si atoms in the bulk and at the edges of single-layer graphene is examined using scanning transmission electron microscopy (STEM). A deep learning network is used to convert experimental STEM movies into coordinates of individual Si and carbon atoms. A Gaussian mixture model is further used to establish the elementary atomic configurations of the Si atoms, defining the bonding geometries and chemical species and accounting for the discrete rotational symmetry of the host lattice. The frequencies and Markov transition probabilities between these states are determined. This analysis enables insight into the defect populations and chemical transformation networks from the atomically resolved STEM data. Here, a clear tendency is observed for the formation of a 1D Si crystal along zigzag direction of graphene edges and for the Si impurity coupling to topological defects in bulk graphene.

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

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

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1607241

Alternate ID(s):: OSTI ID: 1575294

Journal Information:: Advanced Functional Materials, Vol. 29, Issue 52; ISSN 1616-301X

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 21 works

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Quantifying the Dynamics of Protein Self-Organization Using Deep Learning Analysis of Atomic Force Microscopy Data Ziatdinov, Maxim; Zhang, Shuai; Dollar, Orion Nano Letters, Vol. 21, Issue 1 https://doi.org/10.1021/acs.nanolett.0c03447	journal	December 2020
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Unsupervised learning of ferroic variants from atomically resolved STEM images Valleti, Mani; Kalinin, Sergei V.; Nelson, Christopher T. arXiv https://doi.org/10.48550/arxiv.2101.06892	preprint	January 2021

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36 MATERIALS SCIENCE
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Title: Atomic Mechanisms for the Si Atom Dynamics in Graphene: Chemical Transformations at the Edge and in the Bulk

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