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dc.contributor.authorKleiven, David
dc.contributor.authorAkola, Jaakko
dc.date.accessioned2021-09-03T07:22:26Z
dc.date.available2021-09-03T07:22:26Z
dc.date.created2020-10-16T14:21:16Z
dc.date.issued2020
dc.identifier.citationActa Materialia. 2020, 195 123-131.en_US
dc.identifier.issn1359-6454
dc.identifier.urihttps://hdl.handle.net/11250/2772719
dc.description.abstractTernary Al–Mg–Si alloys have been modelled based on a multi-scale approach that spans across atomistic and mesoscale models and uses theoretically determined parameters. First, a cluster expansion model for total energy has been trained for atomistic configurations (FCC lattice) based on the data from density functional simulations of electronic structure. Free energy curves as a function of solute (Mg, Si) concentrations and disorder have been obtained by using this parameterisation together with meta-dynamics Monte Carlo sampling. In addition, free energy data, surface tensions as well as strain energy using the linear elasticity theory have been collected to be combined for a mesoscale phase-field model. The application of this approach shows that the formation of a layered MgSi phase, with (100) planes, is a particularly stable solute aggregation motif within the Al host matrix. Moreover, the phase-field model demonstrates that the preferred shape of the MgSi precipitates is needle-like (in FCC), and they can act as precursors for the important and well-known β″-type precipitates which are formed by translating one Mg column by a 1/2 lattice vector. The results provide theoretical evidence that the solute aggregation into needle-like MgSi domains (precipitates) is an inherent property of Al-Mg-Si alloys, and that it takes place even without the presence of vacancies which is a precondition for the eventual formation β″ precipitates.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titlePrecipitate formation in aluminium alloys: Multi-scale modelling approachen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber123-131en_US
dc.source.volume195en_US
dc.source.journalActa Materialiaen_US
dc.identifier.doi10.1016/j.actamat.2020.05.050
dc.identifier.cristin1840197
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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