Modeling the first stages of Cu precipitation in α-Fe using a hybrid atomistic kinetic Monte Carlo approach

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Modeling the first stages of Cu precipitation in α-Fe using a hybrid atomistic kinetic Monte Carlo approach. / Castin, Nicolas; Pascuet, Maria Ines; Malerba, Lorenzo; Terentyev, Dmitry (Peer reviewer); Bonny, Giovanni (Peer reviewer).

In: The Journal of Chemical Physics, Vol. 135, No. 6, 08.2011, p. 064502-064502.

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@article{6d2a6889b89349e7842d567b56aa483a,
title = "Modeling the first stages of Cu precipitation in α-Fe using a hybrid atomistic kinetic Monte Carlo approach",
abstract = "We simulate the coherent stage of Cu precipitation in α-Fe with an atomistic kinetic Monte Carlo (AKMC) model. The vacancy migration energy as a function of the local chemical environment is provided on-the-fly by a neural network, trained with high precision on values calculated with the nudged elastic band method, using a suitable interatomic potential. To speed up the simulation, how- ever, we modify the standard AKMC algorithm by treating large Cu clusters as objects, similarly to object kinetic Monte Carlo approaches. Seamless matching between the fully atomistic and the coarse-grained approach is achieved again by using a neural network, that provides all stability and mobility parameters for large Cu clusters, after training on atomistically informed results. The result- ing hybrid algorithm allows long thermal annealing experiments to be simulated, within a reasonable CPU time. The results obtained are in very good agreement with several series of experimental data available from the literature, spanning over different conditions of temperature and alloy composi- tion. We deduce from these results and relevant parametric studies that the mobility of Cu clusters containing one vacancy plays a central role in the precipitation mechanism.",
keywords = "thermal annealing, Fe-Cu alloy, Cu precipitation, multiscale modeling",
author = "Nicolas Castin and Pascuet, {Maria Ines} and Lorenzo Malerba and Dmitry Terentyev and Giovanni Bonny",
note = "Score = 10",
year = "2011",
month = "8",
doi = "10.1063/1.3622045",
language = "English",
volume = "135",
pages = "064502--064502",
journal = "The Journal of Chemical Physics",
issn = "1089-7690",
publisher = "AIP - American Institute of Physics",
number = "6",

}

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TY - JOUR

T1 - Modeling the first stages of Cu precipitation in α-Fe using a hybrid atomistic kinetic Monte Carlo approach

AU - Castin, Nicolas

AU - Pascuet, Maria Ines

AU - Malerba, Lorenzo

A2 - Terentyev, Dmitry

A2 - Bonny, Giovanni

N1 - Score = 10

PY - 2011/8

Y1 - 2011/8

N2 - We simulate the coherent stage of Cu precipitation in α-Fe with an atomistic kinetic Monte Carlo (AKMC) model. The vacancy migration energy as a function of the local chemical environment is provided on-the-fly by a neural network, trained with high precision on values calculated with the nudged elastic band method, using a suitable interatomic potential. To speed up the simulation, how- ever, we modify the standard AKMC algorithm by treating large Cu clusters as objects, similarly to object kinetic Monte Carlo approaches. Seamless matching between the fully atomistic and the coarse-grained approach is achieved again by using a neural network, that provides all stability and mobility parameters for large Cu clusters, after training on atomistically informed results. The result- ing hybrid algorithm allows long thermal annealing experiments to be simulated, within a reasonable CPU time. The results obtained are in very good agreement with several series of experimental data available from the literature, spanning over different conditions of temperature and alloy composi- tion. We deduce from these results and relevant parametric studies that the mobility of Cu clusters containing one vacancy plays a central role in the precipitation mechanism.

AB - We simulate the coherent stage of Cu precipitation in α-Fe with an atomistic kinetic Monte Carlo (AKMC) model. The vacancy migration energy as a function of the local chemical environment is provided on-the-fly by a neural network, trained with high precision on values calculated with the nudged elastic band method, using a suitable interatomic potential. To speed up the simulation, how- ever, we modify the standard AKMC algorithm by treating large Cu clusters as objects, similarly to object kinetic Monte Carlo approaches. Seamless matching between the fully atomistic and the coarse-grained approach is achieved again by using a neural network, that provides all stability and mobility parameters for large Cu clusters, after training on atomistically informed results. The result- ing hybrid algorithm allows long thermal annealing experiments to be simulated, within a reasonable CPU time. The results obtained are in very good agreement with several series of experimental data available from the literature, spanning over different conditions of temperature and alloy composi- tion. We deduce from these results and relevant parametric studies that the mobility of Cu clusters containing one vacancy plays a central role in the precipitation mechanism.

KW - thermal annealing

KW - Fe-Cu alloy

KW - Cu precipitation

KW - multiscale modeling

UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/ezp_115283

UR - http://knowledgecentre.sckcen.be/so2/bibref/8229

U2 - 10.1063/1.3622045

DO - 10.1063/1.3622045

M3 - Article

VL - 135

SP - 64502

EP - 64502

JO - The Journal of Chemical Physics

JF - The Journal of Chemical Physics

SN - 1089-7690

IS - 6

ER -

ID: 290913