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Data sets of migration barriers for atomistic Kinetic Monte Carlo simulations of Cu self-diffusion via first nearest neighbour atomic jumps.
Baibuz, Ekaterina; Vigonski, Simon; Lahtinen, Jyri; Zhao, Junlei; Jansson, Ville; Zadin, Vahur; Djurabekova, Flyura.
Afiliação
  • Baibuz E; Helsinki Institute of Physics and Department of Physics, University of Helsinki, P.O. Box 43, Pietari Kalmin Katu 2, FI-00014, Finland.
  • Vigonski S; Helsinki Institute of Physics and Department of Physics, University of Helsinki, P.O. Box 43, Pietari Kalmin Katu 2, FI-00014, Finland.
  • Lahtinen J; Institute of Technology, University of Tartu, Nooruse 1, 50411 Tartu, Estonia.
  • Zhao J; Helsinki Institute of Physics and Department of Physics, University of Helsinki, P.O. Box 43, Pietari Kalmin Katu 2, FI-00014, Finland.
  • Jansson V; Helsinki Institute of Physics and Department of Physics, University of Helsinki, P.O. Box 43, Pietari Kalmin Katu 2, FI-00014, Finland.
  • Zadin V; Helsinki Institute of Physics and Department of Physics, University of Helsinki, P.O. Box 43, Pietari Kalmin Katu 2, FI-00014, Finland.
  • Djurabekova F; Helsinki Institute of Physics and Department of Physics, University of Helsinki, P.O. Box 43, Pietari Kalmin Katu 2, FI-00014, Finland.
Data Brief ; 17: 739-743, 2018 Apr.
Article em En | MEDLINE | ID: mdl-29876431
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 have calculated three data sets of migration barriers for Cu self-diffusion with two different methods. The data sets were specifically calculated for rigid lattice KMC simulations of copper self-diffusion on arbitrarily rough surfaces, but can be used for KMC simulations of bulk diffusion as well.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2018 Tipo de documento: Article