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Title: A hybrid optimization algorithm to explore atomic configurations of TiO 2 nanoparticles

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 nO 2n 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 lowermore » energies per TiO 2 unit as the system size increases.« less
Authors:
 [1] ; ORCiD logo [2] ; ORCiD logo [3]
  1. Georgia Inst. of Technology, Atlanta, GA (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS)
  3. 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:
Grant/Contract Number:
AC05-00OR22725; AC02-05CH11231
Type:
Accepted Manuscript
Journal Name:
Computational Materials Science
Additional Journal Information:
Journal Volume: 141; Journal ID: ISSN 0927-0256
Publisher:
Elsevier
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); National Research Foundation of Korea (NRF)
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; Global structure search algorithm; Differential evolution; Hybrid algorithm; Atomistic simulations; Titanium dioxide (TiO2)
OSTI Identifier:
1429207

Inclan, Eric J., Geohegan, David B., and Yoon, Mina. A hybrid optimization algorithm to explore atomic configurations of TiO2 nanoparticles. United States: N. p., Web. doi:10.1016/j.commatsci.2017.08.046.
Inclan, Eric J., Geohegan, David B., & Yoon, Mina. A hybrid optimization algorithm to explore atomic configurations of TiO2 nanoparticles. United States. doi:10.1016/j.commatsci.2017.08.046.
Inclan, Eric J., Geohegan, David B., and Yoon, Mina. 2017. "A hybrid optimization algorithm to explore atomic configurations of TiO2 nanoparticles". United States. doi:10.1016/j.commatsci.2017.08.046. https://www.osti.gov/servlets/purl/1429207.
@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},
journal = {Computational Materials Science},
number = ,
volume = 141,
place = {United States},
year = {2017},
month = {10}
}