Stability and mobility of small and large Cu-vacancy clusters in FeCu alloys using an artificial intelligence driven atomistic kinetic Monte Carlo method

Ines Pascuet, Lorenzo Malerba, Nicolas Castin, Giovanni Bonny

    Research outputpeer-review


    Information on point-defect cluster stability and mobility is needed in order to parameterise models describing the nanostructure evolution of reactor pressure vessel steels under irradiation, but cannot be obtained directly from experimental measurements. The stability and mobility of vacancy-containing copper clusters and precipitates in iron has been quantitatively studied using advanced atomistic kinetic Monte Carlo methods, whose physical reliability has been improved by employing artificial intelligence techniques for the regression of the activation energies required by the model as input. Thus, the model validation was based, on the one hand, on comparison with available ab-initio (quantum mechanical) calculation for the verification of the used cohesive model, and, on the other, with other models and theories. The direct validation on experimental studies of copper precipitation appears to be computationally challenging, due to space- and time-scale limitations of the model.
    Original languageEnglish
    PublisherSCK CEN
    Number of pages50
    StatePublished - May 2009

    Publication series

    NameSCK•CEN Reports
    PublisherStudiecentrum voor Kernenergie

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