A hybrid optimization algorithm to explore atomic configurations of TiO _{2} nanoparticles
Abstract
Here in this paper we present a hybrid algorithm comprised of differential evolution, coupled with the Broyden–Fletcher–Goldfarb–Shanno quasiNewton optimization algorithm, for the purpose of identifying a broad range of (meta)stable Ti _{n}O _{2}n nanoparticles, as an example system, described by Buckingham interatomic potential. The potential and its gradient are modified to be piecewise continuous to enable use of these continuousdomain, 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 _{n}O _{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 »
 Authors:

 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:
 Research Org.:
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Sponsoring Org.:
 USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC22); National Research Foundation of Korea (NRF)
 OSTI Identifier:
 1429207
 Alternate Identifier(s):
 OSTI ID: 1496343
 Grant/Contract Number:
 AC0500OR22725; AC0205CH11231
 Resource Type:
 Accepted Manuscript
 Journal Name:
 Computational Materials Science
 Additional Journal Information:
 Journal Volume: 141; Journal ID: ISSN 09270256
 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)
Citation Formats
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.
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. Tue .
"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 quasiNewton 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 piecewise continuous to enable use of these continuousdomain, 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}
}