A hybrid optimization algorithm to explore atomic configurations of TiO2 nanoparticles

Inclan, Eric J.; Geohegan, David B.; Yoon, Mina

doi:10.1016/j.commatsci.2017.08.046

Title: A hybrid optimization algorithm to explore atomic configurations of TiO₂ nanoparticles

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Abstract

Here in this paper we present a hybrid algorithm comprised of differential evolution, coupled with the Broyden–Fletcher–Goldfarb–Shanno quasi-Newton optimization algorithm, for the purpose of identifying a broad range of (meta)stable Ti_nO₂n nanoparticles, as an example system, described by Buckingham interatomic potential. The potential and its gradient are modified to be piece-wise continuous to enable use of these continuous-domain, unconstrained algorithms, thereby improving compatibility. To measure computational effectiveness a regression on known structures is used. This approach defines effectiveness as the ability of an algorithm to produce a set of structures whose energy distribution follows the regression as the number of Ti_nO_2n increases such that the shape of the distribution is consistent with the algorithm’s stated goals. Our calculation demonstrates that the hybrid algorithm finds global minimum configurations more effectively than the differential evolution algorithms, widely employed in the field of materials science. Specifically, the hybrid algorithm is shown to reproduce the global minimum energy structures reported in the literature up to n = 5, and retains good agreement with the regression up to n = 25. For 25 < n < 100, where literature structures are unavailable, the hybrid effectively obtains structures that are in lower energies per TiO₂ unitmore » as the system size increases.« less

Authors:

Inclan, Eric J. ^[1];

^[2];

^[3]

Georgia Inst. of Technology, Atlanta, GA (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS); Univ. of Tennessee, Knoxville, TN (United States). Dept. of Physics and Astronomy

Publication Date:: Tue Oct 17 00:00:00 EDT 2017

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Basic Energy Sciences (BES); National Research Foundation of Korea (NRF)

OSTI Identifier:: 1429207

Alternate Identifier(s):: OSTI ID: 1496343

Grant/Contract Number:: AC05-00OR22725; AC02-05CH11231

Resource Type:: Journal Article: Accepted Manuscript

Journal Name:: Computational Materials Science

Additional Journal Information:: Journal Volume: 141; Journal ID: ISSN 0927-0256

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 36 MATERIALS SCIENCE; Global structure search algorithm; Differential evolution; Hybrid algorithm; Atomistic simulations; Titanium dioxide (TiO2)

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                    Inclan, Eric J., Geohegan, David B., and Yoon, Mina. A hybrid optimization algorithm to explore atomic configurations of TiO2 nanoparticles.  United States: N. p., 2017. 
        Web.  doi:10.1016/j.commatsci.2017.08.046.

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                    Inclan, Eric J., Geohegan, David B., & Yoon, Mina. A hybrid optimization algorithm to explore atomic configurations of TiO2 nanoparticles.  United States.  https://doi.org/10.1016/j.commatsci.2017.08.046

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                    Inclan, Eric J., Geohegan, David B., and Yoon, Mina. 2017.  
        "A hybrid optimization algorithm to explore atomic configurations of TiO2 nanoparticles".  United States.  https://doi.org/10.1016/j.commatsci.2017.08.046.  https://www.osti.gov/servlets/purl/1429207.

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@article{osti_1429207,

  title        = {A hybrid optimization algorithm to explore atomic configurations of TiO2 nanoparticles},

  author       = {Inclan, Eric J. and Geohegan, David B. and Yoon, Mina},

  abstractNote = {Here in this paper we present a hybrid algorithm comprised of differential evolution, coupled with the Broyden–Fletcher–Goldfarb–Shanno quasi-Newton optimization algorithm, for the purpose of identifying a broad range of (meta)stable TinO2n nanoparticles, as an example system, described by Buckingham interatomic potential. The potential and its gradient are modified to be piece-wise continuous to enable use of these continuous-domain, unconstrained algorithms, thereby improving compatibility. To measure computational effectiveness a regression on known structures is used. This approach defines effectiveness as the ability of an algorithm to produce a set of structures whose energy distribution follows the regression as the number of TinO2n increases such that the shape of the distribution is consistent with the algorithm’s stated goals. Our calculation demonstrates that the hybrid algorithm finds global minimum configurations more effectively than the differential evolution algorithms, widely employed in the field of materials science. Specifically, the hybrid algorithm is shown to reproduce the global minimum energy structures reported in the literature up to n = 5, and retains good agreement with the regression up to n = 25. For 25 < n < 100, where literature structures are unavailable, the hybrid effectively obtains structures that are in lower energies per TiO2 unit as the system size increases.},

  doi          = {10.1016/j.commatsci.2017.08.046},

  url          = {https://www.osti.gov/biblio/1429207},
  journal      = {Computational Materials Science},

  issn         = {0927-0256},

  number       = ,

  volume       = 141,

  place        = {United States},

  year         = {Tue Oct 17 00:00:00 EDT 2017},

  month        = {Tue Oct 17 00:00:00 EDT 2017}

}

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Title: A hybrid optimization algorithm to explore atomic configurations of TiO2 nanoparticles

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Title: A hybrid optimization algorithm to explore atomic configurations of TiO₂ nanoparticles