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dc.contributor.authorArapan, Sergiu 
dc.contributor.authorNieves Cordones, Pablo 
dc.contributor.authorCuesta López, Santiago 
dc.date.accessioned2018-03-21T08:33:21Z
dc.date.available2018-03-21T08:33:21Z
dc.date.issued2018-02
dc.identifier.issn0021-8979
dc.identifier.urihttp://hdl.handle.net/10259/4761
dc.description.abstractWe study the capability of a structure predicting method based on genetic/evolutionary algorithm for a high-throughput exploration of magnetic materials. We use the USPEX and VASP codes to predict stable and generate low-energy meta-stable structures for a set of representative magnetic structures comprising intermetallic alloys, oxides, interstitial compounds, and systems containing rare-earths elements, and for both types of ferromagnetic and antiferromagnetic ordering. We have modified the interface between USPEX and VASP codes to improve the performance of structural optimization as well as to perform calculations in a high-throughput manner. We show that exploring the structure phase space with a structure predicting technique reveals large sets of low-energy metastable structures, which not only improve currently exiting databases, but also may provide understanding and solutions to stabilize and synthesize magnetic materials suitable for permanent magnet applications.en
dc.description.sponsorshipEU H2020 Program Project NOVAMAG: Novel, critical materials free, high anisotropy phases for permanent magnets, by design (Project ID: 686056).en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherAIP Publishingen
dc.relation.ispartofJournal of Applied Physics. 2018, V. 123, n. 8, 083904en
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleA high-throughput exploration of magnetic materials by using structure predicting methodsen
dc.typeArtículoes
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.relation.publisherversionhttps://doi.org/10.1063/1.5004979
dc.identifier.doi10.1063/1.5004979
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/686056
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen


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