Application of artificial neural networks for rigid lattice kinetic Monte Carlo studies of Cu surface diffusion

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dc.contributor University of Helsinki, Helsinki Institute of Physics en
dc.contributor University of Helsinki, Helsinki Institute of Physics en
dc.contributor University of Helsinki, Department of Physics en
dc.contributor University of Helsinki, Helsinki Institute of Physics en
dc.contributor University of Helsinki, Department of Physics en
dc.contributor.author Kimari, Jyri
dc.contributor.author Jansson, Ville
dc.contributor.author Vigonski, Simon
dc.contributor.author Baibuz, Ekaterina
dc.contributor.author Domingos, Roberto
dc.contributor.author Zadin, Vahur
dc.contributor.author Djurabekova, Flyura
dc.date.accessioned 2020-08-31T10:03:02Z
dc.date.available 2020-08-31T10:03:02Z
dc.date.issued 2020-10
dc.identifier.citation Kimari , J , Jansson , V , Vigonski , S , Baibuz , E , Domingos , R , Zadin , V & Djurabekova , F 2020 , ' Application of artificial neural networks for rigid lattice kinetic Monte Carlo studies of Cu surface diffusion ' , Computational Materials Science , vol. 183 , 109789 . https://doi.org/10.1016/j.commatsci.2020.109789 en
dc.identifier.issn 0927-0256
dc.identifier.other PURE: 120786059
dc.identifier.other PURE UUID: f62d7307-49b1-442c-b63c-ff48cddef02e
dc.identifier.other WOS: 000558686500001
dc.identifier.other ORCID: /0000-0002-5828-200X/work/79876469
dc.identifier.other ORCID: /0000-0001-6560-9982/work/79877217
dc.identifier.other ORCID: /0000-0002-9099-1455/work/79880065
dc.identifier.uri http://hdl.handle.net/10138/318844
dc.description.abstract Kinetic Monte Carlo (KMC) is a powerful method for simulation of diffusion processes in various systems. The accuracy of the method, however, relies on the extent of details used for the parameterization of the model. Migration barriers are often used to describe diffusion on atomic scale, but the full set of these barriers may become easily unmanageable in materials with increased chemical complexity or a large number of defects. This work is a feasibility study for applying a machine learning approach for Cu surface diffusion. We train an artificial neural network on a subset of the large set of 2(26) barriers needed to correctly describe the surface diffusion in Cu. Our KMC simulations using the obtained barrier predictor show sufficient accuracy in modelling processes on the low-index surfaces and display the correct thermodynamical stability of these surfaces. en
dc.format.extent 11
dc.language.iso eng
dc.relation.ispartof Computational Materials Science
dc.relation.uri https://arxiv.org/abs/1806.02976
dc.rights en
dc.subject 114 Physical sciences en
dc.subject 113 Computer and information sciences en
dc.title Application of artificial neural networks for rigid lattice kinetic Monte Carlo studies of Cu surface diffusion en
dc.type Article
dc.description.version Peer reviewed
dc.identifier.doi https://doi.org/10.1016/j.commatsci.2020.109789
dc.type.uri info:eu-repo/semantics/other
dc.type.uri info:eu-repo/semantics/publishedVersion
dc.contributor.pbl
dc.contributor.pbl

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