Stability and mobility of Cu–vacancy clusters in Fe–Cu alloys: A computational study based on the use of artificial neural networks for energy barrier calculations

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@article{c78a6cfdb615461f909ee84643a8d0bb,
title = "Stability and mobility of Cu–vacancy clusters in Fe–Cu alloys: A computational study based on the use of artificial neural networks for energy barrier calculations",
abstract = "An atomistic kinetic Monte Carlo (AKMC) method has been applied to study the stability and mobility of copper–vacancy clusters in Fe. This information, which cannot be obtained directly from experimental measurements, is needed to parameterise models describing the nanostructure evolution under irradiation of Fe alloys (e.g. model alloys for reactor pressure vessel steels). The physical reliability of the AKMC method has been improved by employing artificial intelligence techniques for the regression of the activation energies required by the model as input. These energies are calculated allowing for the effects of local chemistry and relaxation, using an interatomic potential fitted to reproduce them as accurately as possible and the nudged-elastic-band method. The model validation was based on comparison with available ab initio calculations for verification of the used cohesive model, as well as with other models and theories.",
keywords = "Object kinetic Monte Carlo, Diffusion coefficients, Artificial neural network",
author = "Pascuet, {Maria Ines} and Nicolas Castin and Charlotte Becquart and Lorenzo Malerba and Dmitry Terentyev and Giovanni Bonny",
note = "Score = 10",
year = "2011",
month = "5",
day = "1",
doi = "10.1016/j.jnucmat.2011.02.038",
language = "English",
volume = "421",
pages = "106--115",
journal = "Journal of Nuclear Materials",
issn = "0022-3115",
publisher = "Elsevier",
number = "1",

}

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TY - JOUR

T1 - Stability and mobility of Cu–vacancy clusters in Fe–Cu alloys: A computational study based on the use of artificial neural networks for energy barrier calculations

AU - Pascuet, Maria Ines

AU - Castin, Nicolas

AU - Becquart, Charlotte

AU - Malerba, Lorenzo

A2 - Terentyev, Dmitry

A2 - Bonny, Giovanni

N1 - Score = 10

PY - 2011/5/1

Y1 - 2011/5/1

N2 - An atomistic kinetic Monte Carlo (AKMC) method has been applied to study the stability and mobility of copper–vacancy clusters in Fe. This information, which cannot be obtained directly from experimental measurements, is needed to parameterise models describing the nanostructure evolution under irradiation of Fe alloys (e.g. model alloys for reactor pressure vessel steels). The physical reliability of the AKMC method has been improved by employing artificial intelligence techniques for the regression of the activation energies required by the model as input. These energies are calculated allowing for the effects of local chemistry and relaxation, using an interatomic potential fitted to reproduce them as accurately as possible and the nudged-elastic-band method. The model validation was based on comparison with available ab initio calculations for verification of the used cohesive model, as well as with other models and theories.

AB - An atomistic kinetic Monte Carlo (AKMC) method has been applied to study the stability and mobility of copper–vacancy clusters in Fe. This information, which cannot be obtained directly from experimental measurements, is needed to parameterise models describing the nanostructure evolution under irradiation of Fe alloys (e.g. model alloys for reactor pressure vessel steels). The physical reliability of the AKMC method has been improved by employing artificial intelligence techniques for the regression of the activation energies required by the model as input. These energies are calculated allowing for the effects of local chemistry and relaxation, using an interatomic potential fitted to reproduce them as accurately as possible and the nudged-elastic-band method. The model validation was based on comparison with available ab initio calculations for verification of the used cohesive model, as well as with other models and theories.

KW - Object kinetic Monte Carlo

KW - Diffusion coefficients

KW - Artificial neural network

UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/ezp_113643

UR - http://knowledgecentre.sckcen.be/so2/bibref/8031

U2 - 10.1016/j.jnucmat.2011.02.038

DO - 10.1016/j.jnucmat.2011.02.038

M3 - Article

VL - 421

SP - 106

EP - 115

JO - Journal of Nuclear Materials

JF - Journal of Nuclear Materials

SN - 0022-3115

IS - 1

ER -

ID: 346015