Elsevier
Computational Materials Science
,  том 155 ,  страницы 209-215

Efficient energy basin finding method for atomistic kinetic Monte Carlo models

Тип публикации: Journal Article
Дата публикации: 2018-12-01
Elsevier
scimago Q1
wos Q2
БС1
SJR: 0.782
CiteScore: 6.6
Impact factor: 3.3
ISSN: 09270256, 18790801
General Chemistry
General Physics and Astronomy
General Materials Science
Mechanics of Materials
Computational Mathematics
General Computer Science
Краткое описание
Presence of energy basins significantly slows down simulations with the kinetic Monte Carlo (kMC) method. Various methods of the kMC acceleration are available nowadays, but all of them require efficient energy basin finding algorithm. We present the algorithm providing significant acceleration of the kMC calculations. Use of the acceleration speeds up calculations and allows to reach experimental timescales in simulations. During the simulation of the Pt/Cu(1 1 1) surface alloy formation the acceleration is greater than 5000 times. Results of our simulation qualitatively agree with experiment.
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Dokukin S. A., Kolesnikov S. V., Saletsky A. M. Efficient energy basin finding method for atomistic kinetic Monte Carlo models // Computational Materials Science. 2018. Vol. 155. pp. 209-215.
ГОСТ со всеми авторами (до 50) Скопировать
Dokukin S. A., Kolesnikov S. V., Saletsky A. M. Efficient energy basin finding method for atomistic kinetic Monte Carlo models // Computational Materials Science. 2018. Vol. 155. pp. 209-215.
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TY - JOUR
DO - 10.1016/j.commatsci.2018.08.045
UR - https://doi.org/10.1016/j.commatsci.2018.08.045
TI - Efficient energy basin finding method for atomistic kinetic Monte Carlo models
T2 - Computational Materials Science
AU - Dokukin, S A
AU - Kolesnikov, S. V.
AU - Saletsky, A. M.
PY - 2018
DA - 2018/12/01
PB - Elsevier
SP - 209-215
VL - 155
SN - 0927-0256
SN - 1879-0801
ER -
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@article{2018_Dokukin,
author = {S A Dokukin and S. V. Kolesnikov and A. M. Saletsky},
title = {Efficient energy basin finding method for atomistic kinetic Monte Carlo models},
journal = {Computational Materials Science},
year = {2018},
volume = {155},
publisher = {Elsevier},
month = {dec},
url = {https://doi.org/10.1016/j.commatsci.2018.08.045},
pages = {209--215},
doi = {10.1016/j.commatsci.2018.08.045}
}