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

Nicolas Castin, Roberto Domingos, Lorenzo Malerba, Dmitry Terentyev, Giovanni Bonny

    Research outputpeer-review


    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.
    Original languageEnglish
    Pages (from-to)340-352
    JournalInternational Journal of Computational Intelligence Systems
    Issue number4
    StatePublished - Dec 2008

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