Prediction of point-defect migration energy barriers in alloys using artificial intelligence for atomistic kinetic Monte Carlo applications

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Prediction of point-defect migration energy barriers in alloys using artificial intelligence for atomistic kinetic Monte Carlo applications. / Castin, Nicolas; Malerba, Lorenzo; Bonny, Giovanni (Peer reviewer).

In: Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, Vol. 267, 06.2009, p. 3148-3151.

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Castin, N, Malerba, L & Bonny, G 2009, 'Prediction of point-defect migration energy barriers in alloys using artificial intelligence for atomistic kinetic Monte Carlo applications', Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, vol. 267, pp. 3148-3151. https://doi.org/10.1016/j.nimb.2009.06.041

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Castin, Nicolas ; Malerba, Lorenzo ; Bonny, Giovanni. / Prediction of point-defect migration energy barriers in alloys using artificial intelligence for atomistic kinetic Monte Carlo applications. In: Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms. 2009 ; Vol. 267. pp. 3148-3151.

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@article{6fd552a16c8b4e7b93ae034ba577d6f8,
title = "Prediction of point-defect migration energy barriers in alloys using artificial intelligence for atomistic kinetic Monte Carlo applications",
abstract = "We significantly improved a previously proposed method to take into account chemical and also relaxation effects on point-defect migration energy barriers, as predicted by an interatomic potential, in a rigid lattice atomistic kinetic Monte Carlo simulation. Examples of energy barriers are rigorously calculated, including chemical and relaxation effects, as functions of the local atomic configuration, using a nudged elastic bands technique. These examples are then used to train an artificial neural network that provides the barriers on-demand during the simulation for each configuration encountered by the migrating defect. Thanks to a newly developed training method, the configuration can include a large number of neighbour shells, thereby properly including also strain effects. Satisfactory results have been obtained when the configuration includes different chemical species only. The problems encountered in the extension of the method to configurations including any number of point-defects are stated and solutions to tackle them are sketched.",
keywords = "Artificial intelligence, atomistic kinetic Monte Carlo, chemical and relaxation effects",
author = "Nicolas Castin and Lorenzo Malerba and Giovanni Bonny",
note = "Score = 10",
year = "2009",
month = "6",
doi = "10.1016/j.nimb.2009.06.041",
language = "English",
volume = "267",
pages = "3148--3151",
journal = "Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms",
issn = "0168-583X",
publisher = "Elsevier",

}

RIS - Download

TY - JOUR

T1 - Prediction of point-defect migration energy barriers in alloys using artificial intelligence for atomistic kinetic Monte Carlo applications

AU - Castin, Nicolas

AU - Malerba, Lorenzo

A2 - Bonny, Giovanni

N1 - Score = 10

PY - 2009/6

Y1 - 2009/6

N2 - We significantly improved a previously proposed method to take into account chemical and also relaxation effects on point-defect migration energy barriers, as predicted by an interatomic potential, in a rigid lattice atomistic kinetic Monte Carlo simulation. Examples of energy barriers are rigorously calculated, including chemical and relaxation effects, as functions of the local atomic configuration, using a nudged elastic bands technique. These examples are then used to train an artificial neural network that provides the barriers on-demand during the simulation for each configuration encountered by the migrating defect. Thanks to a newly developed training method, the configuration can include a large number of neighbour shells, thereby properly including also strain effects. Satisfactory results have been obtained when the configuration includes different chemical species only. The problems encountered in the extension of the method to configurations including any number of point-defects are stated and solutions to tackle them are sketched.

AB - We significantly improved a previously proposed method to take into account chemical and also relaxation effects on point-defect migration energy barriers, as predicted by an interatomic potential, in a rigid lattice atomistic kinetic Monte Carlo simulation. Examples of energy barriers are rigorously calculated, including chemical and relaxation effects, as functions of the local atomic configuration, using a nudged elastic bands technique. These examples are then used to train an artificial neural network that provides the barriers on-demand during the simulation for each configuration encountered by the migrating defect. Thanks to a newly developed training method, the configuration can include a large number of neighbour shells, thereby properly including also strain effects. Satisfactory results have been obtained when the configuration includes different chemical species only. The problems encountered in the extension of the method to configurations including any number of point-defects are stated and solutions to tackle them are sketched.

KW - Artificial intelligence

KW - atomistic kinetic Monte Carlo

KW - chemical and relaxation effects

UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/ezp_101742

UR - http://knowledgecentre.sckcen.be/so2/bibref/6335

U2 - 10.1016/j.nimb.2009.06.041

DO - 10.1016/j.nimb.2009.06.041

M3 - Article

VL - 267

SP - 3148

EP - 3151

JO - Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms

JF - Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms

SN - 0168-583X

ER -

ID: 375203