Stability and mobility of Cu–vacancy clusters in Fe–Cu alloys: A computational study based on the use of artificial neural networks for energy barrier calculations

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Abstract

An atomistic kinetic Monte Carlo (AKMC) method has been applied to study the stability and mobility of copper–vacancy clusters in Fe. This information, which cannot be obtained directly from experimental measurements, is needed to parameterise models describing the nanostructure evolution under irradiation of Fe alloys (e.g. model alloys for reactor pressure vessel steels). The physical reliability of the AKMC method has been improved by employing artificial intelligence techniques for the regression of the activation energies required by the model as input. These energies are calculated allowing for the effects of local chemistry and relaxation, using an interatomic potential fitted to reproduce them as accurately as possible and the nudged-elastic-band method. The model validation was based on comparison with available ab initio calculations for verification of the used cohesive model, as well as with other models and theories.

Details

Original languageEnglish
Pages (from-to)106-115
JournalJournal of Nuclear Materials
Volume421
Issue number1
DOIs
Publication statusPublished - 1 May 2011

Keywords

  • Object kinetic Monte Carlo, Diffusion coefficients, Artificial neural network

ID: 346015