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Data sets of migration barriers for atomistic Kinetic Monte Carlo simulations of Fe self-diffusion

Atomistic rigid lattice Kinetic Monte Carlo (KMC) is an efficient method for simulating nano-objects and surfaces at timescales much longer than those accessible by molecular dynamics. A laborious and non-trivial part of constructing any KMC model is, however, to calculate all migration barriers tha...

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Autores principales: Baibuz, Ekaterina, Vigonski, Simon, Lahtinen, Jyri, Zhao, Junlei, Jansson, Ville, Zadin, Vahur, Djurabekova, Flyura
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5997586/
https://www.ncbi.nlm.nih.gov/pubmed/29900356
http://dx.doi.org/10.1016/j.dib.2018.04.060
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author Baibuz, Ekaterina
Vigonski, Simon
Lahtinen, Jyri
Zhao, Junlei
Jansson, Ville
Zadin, Vahur
Djurabekova, Flyura
author_facet Baibuz, Ekaterina
Vigonski, Simon
Lahtinen, Jyri
Zhao, Junlei
Jansson, Ville
Zadin, Vahur
Djurabekova, Flyura
author_sort Baibuz, Ekaterina
collection PubMed
description Atomistic rigid lattice Kinetic Monte Carlo (KMC) is an efficient method for simulating nano-objects and surfaces at timescales much longer than those accessible by molecular dynamics. A laborious and non-trivial part of constructing any KMC model is, however, to calculate all migration barriers that are needed to give the probabilities for any atom jump event to occur in the simulations. We calculated three data sets of migration barriers for Fe self-diffusion: barriers of first nearest neighbour jumps, second nearest neighbours hop-on jumps on the Fe {100} surface and a set of barriers of the diagonal exchange processes for various cases of the local atomic environments within the 2nn coordination shell.
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spelling pubmed-59975862018-06-13 Data sets of migration barriers for atomistic Kinetic Monte Carlo simulations of Fe self-diffusion Baibuz, Ekaterina Vigonski, Simon Lahtinen, Jyri Zhao, Junlei Jansson, Ville Zadin, Vahur Djurabekova, Flyura Data Brief Materials Science Atomistic rigid lattice Kinetic Monte Carlo (KMC) is an efficient method for simulating nano-objects and surfaces at timescales much longer than those accessible by molecular dynamics. A laborious and non-trivial part of constructing any KMC model is, however, to calculate all migration barriers that are needed to give the probabilities for any atom jump event to occur in the simulations. We calculated three data sets of migration barriers for Fe self-diffusion: barriers of first nearest neighbour jumps, second nearest neighbours hop-on jumps on the Fe {100} surface and a set of barriers of the diagonal exchange processes for various cases of the local atomic environments within the 2nn coordination shell. Elsevier 2018-04-24 /pmc/articles/PMC5997586/ /pubmed/29900356 http://dx.doi.org/10.1016/j.dib.2018.04.060 Text en © 2018 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Materials Science
Baibuz, Ekaterina
Vigonski, Simon
Lahtinen, Jyri
Zhao, Junlei
Jansson, Ville
Zadin, Vahur
Djurabekova, Flyura
Data sets of migration barriers for atomistic Kinetic Monte Carlo simulations of Fe self-diffusion
title Data sets of migration barriers for atomistic Kinetic Monte Carlo simulations of Fe self-diffusion
title_full Data sets of migration barriers for atomistic Kinetic Monte Carlo simulations of Fe self-diffusion
title_fullStr Data sets of migration barriers for atomistic Kinetic Monte Carlo simulations of Fe self-diffusion
title_full_unstemmed Data sets of migration barriers for atomistic Kinetic Monte Carlo simulations of Fe self-diffusion
title_short Data sets of migration barriers for atomistic Kinetic Monte Carlo simulations of Fe self-diffusion
title_sort data sets of migration barriers for atomistic kinetic monte carlo simulations of fe self-diffusion
topic Materials Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5997586/
https://www.ncbi.nlm.nih.gov/pubmed/29900356
http://dx.doi.org/10.1016/j.dib.2018.04.060
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