Predicting growth of graphene nanostructures using high-fidelity atomistic simulations

McCarty, Keven F.; Zhou, Xiaowang; Ward, Donald K.; Schultz, Peter A.; Foster, Michael E.; Bartelt, Norman Charles

doi:10.2172/1221517

Title: Predicting growth of graphene nanostructures using high-fidelity atomistic simulations

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Abstract

In this project we developed t he atomistic models needed to predict how graphene grows when carbon is deposited on metal and semiconductor surfaces. We first calculated energies of many carbon configurations using first principles electronic structure calculations and then used these energies to construct an empirical bond order potentials that enable s comprehensive molecular dynamics simulation of growth. We validated our approach by comparing our predictions to experiments of graphene growth on Ir, Cu and Ge. The robustness of ou r understanding of graphene growth will enable high quality graphene to be grown on novel substrates which will expand the number of potential types of graphene electronic devices.

Authors:

McCarty, Keven F. ^[1]; Zhou, Xiaowang ^[1]; Ward, Donald K. ^[1]; Schultz, Peter A. ^[1]; Foster, Michael E. ^[1]; Bartelt, Norman Charles ^[1]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Publication Date:: Tue Sep 01 00:00:00 EDT 2015

Research Org.:: Sandia National Lab. (SNL-CA), Livermore, CA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1221517

Report Number(s):: SAND2015-7944
603848

DOE Contract Number:: AC04-94AL85000

Resource Type:: Technical Report

Country of Publication:: United States

Language:: English

Citation Formats


                    McCarty, Keven F., Zhou, Xiaowang, Ward, Donald K., Schultz, Peter A., Foster, Michael E., and Bartelt, Norman Charles. Predicting growth of graphene nanostructures using high-fidelity atomistic simulations.  United States: N. p., 2015. 
        Web.  doi:10.2172/1221517.

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                    McCarty, Keven F., Zhou, Xiaowang, Ward, Donald K., Schultz, Peter A., Foster, Michael E., & Bartelt, Norman Charles. Predicting growth of graphene nanostructures using high-fidelity atomistic simulations.  United States.  https://doi.org/10.2172/1221517

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                    McCarty, Keven F., Zhou, Xiaowang, Ward, Donald K., Schultz, Peter A., Foster, Michael E., and Bartelt, Norman Charles. 2015.  
        "Predicting growth of graphene nanostructures using high-fidelity atomistic simulations".  United States.  https://doi.org/10.2172/1221517.  https://www.osti.gov/servlets/purl/1221517.

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@article{osti_1221517,

  title        = {Predicting growth of graphene nanostructures using high-fidelity atomistic simulations},

  author       = {McCarty, Keven F. and Zhou, Xiaowang and Ward, Donald K. and Schultz, Peter A. and Foster, Michael E. and Bartelt, Norman Charles},

  abstractNote = {In this project we developed t he atomistic models needed to predict how graphene grows when carbon is deposited on metal and semiconductor surfaces. We first calculated energies of many carbon configurations using first principles electronic structure calculations and then used these energies to construct an empirical bond order potentials that enable s comprehensive molecular dynamics simulation of growth. We validated our approach by comparing our predictions to experiments of graphene growth on Ir, Cu and Ge. The robustness of ou r understanding of graphene growth will enable high quality graphene to be grown on novel substrates which will expand the number of potential types of graphene electronic devices.},

  doi          = {10.2172/1221517},

  url          = {https://www.osti.gov/biblio/1221517},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue Sep 01 00:00:00 EDT 2015},

  month        = {Tue Sep 01 00:00:00 EDT 2015}

}

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Technical Report:

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https://doi.org/10.2172/1221517

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