Use of computational intelligence for the prediction of vacancy migration energies in atomistic kinetic monte carlo simulations

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Use of computational intelligence for the prediction of vacancy migration energies in atomistic kinetic monte carlo simulations. / Castin, Nicolas; Domingos, Roberto; Malerba, Lorenzo; Terentyev, Dmitry (Peer reviewer); Bonny, Giovanni (Peer reviewer).

In: International Journal of Computational Intelligence Systems, Vol. 1, No. 4, 12.2008, p. 340-352.

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@article{2da1556df7ee46a09eb729a5c6f2340c,
title = "Use of computational intelligence for the prediction of vacancy migration energies in atomistic kinetic monte carlo simulations",
abstract = "In this work, we try to build a regression tool to partially replace the use of CPU-time consuming atomic-level procedures for the calculation of point-defect migration energies in Atomistic Kinetic Monte Carlo simulations, as functions of the Local Atomic Configuration. Two approaches are considered : the Cluster Expansion and the Artificial Neural Network. The first one is found to be unpromising because of its high computational complexity. On the contrary, the second provides very encouraging results and is found to be very well behaved.",
keywords = "Neural Networks, Fuzzy Logic, Cluster Expansion, Vacancy Migration Energy",
author = "Nicolas Castin and Roberto Domingos and Lorenzo Malerba and Dmitry Terentyev and Giovanni Bonny",
note = "Score = 10",
year = "2008",
month = "12",
doi = "10.2991/ijcis.2008.1.4.6",
language = "English",
volume = "1",
pages = "340--352",
journal = "International Journal of Computational Intelligence Systems",
issn = "1875-6891",
publisher = "Taylor & Francis (CRC)",
number = "4",

}

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TY - JOUR

T1 - Use of computational intelligence for the prediction of vacancy migration energies in atomistic kinetic monte carlo simulations

AU - Castin, Nicolas

AU - Domingos, Roberto

AU - Malerba, Lorenzo

A2 - Terentyev, Dmitry

A2 - Bonny, Giovanni

N1 - Score = 10

PY - 2008/12

Y1 - 2008/12

N2 - In this work, we try to build a regression tool to partially replace the use of CPU-time consuming atomic-level procedures for the calculation of point-defect migration energies in Atomistic Kinetic Monte Carlo simulations, as functions of the Local Atomic Configuration. Two approaches are considered : the Cluster Expansion and the Artificial Neural Network. The first one is found to be unpromising because of its high computational complexity. On the contrary, the second provides very encouraging results and is found to be very well behaved.

AB - In this work, we try to build a regression tool to partially replace the use of CPU-time consuming atomic-level procedures for the calculation of point-defect migration energies in Atomistic Kinetic Monte Carlo simulations, as functions of the Local Atomic Configuration. Two approaches are considered : the Cluster Expansion and the Artificial Neural Network. The first one is found to be unpromising because of its high computational complexity. On the contrary, the second provides very encouraging results and is found to be very well behaved.

KW - Neural Networks

KW - Fuzzy Logic

KW - Cluster Expansion

KW - Vacancy Migration Energy

UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/ezp_94196

UR - http://knowledgecentre.sckcen.be/so2/bibref/5503

U2 - 10.2991/ijcis.2008.1.4.6

DO - 10.2991/ijcis.2008.1.4.6

M3 - Article

VL - 1

SP - 340

EP - 352

JO - International Journal of Computational Intelligence Systems

JF - International Journal of Computational Intelligence Systems

SN - 1875-6891

IS - 4

ER -

ID: 90040