Segregation of Phosphorus and Silicon at the Grain Boundary in Bcc Iron via Machine-Learned Force Fields

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dc.contributor.authorČerný, Miroslavcs
dc.contributor.authorŠesták, Petrcs
dc.coverage.issue1cs
dc.coverage.volume14cs
dc.date.issued2024-01-12cs
dc.description.abstractThe study of the effects of impurity on grain boundaries is a critical aspect of materials science, particularly when it comes to understanding and controlling the properties of materials for specific applications. One of the related key issues is the segregation preference of impurity atoms in the grain boundary region. In this paper, we employed the on-the-fly machine learning to generate force fields, which were subsequently used to calculate the segregation energies of phosphorus and silicon in bcc iron containing the n-ary sumation 5(310)[001] grain boundary. The generated force fields were successfully benchmarked using ab initio data. Our further calculations considered impurity atoms at a number of possible interstitial and substitutional segregation sites. Our predictions of the preferred sites agree with the experimental observations. Planar concentration of impurity atoms affects the segregation energy and, moreover, can change the preferred segregation sites.en
dc.formattextcs
dc.format.extent11cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationCrystals. 2024, vol. 14, issue 1, 11 p.en
dc.identifier.doi10.3390/cryst14010074cs
dc.identifier.issn2073-4352cs
dc.identifier.orcid0000-0003-0235-8973cs
dc.identifier.orcid0000-0001-8172-1683cs
dc.identifier.other188350cs
dc.identifier.researcheridB-6259-2008cs
dc.identifier.researcheridD-8514-2012cs
dc.identifier.scopus7004499962cs
dc.identifier.scopus23976608700cs
dc.identifier.urihttp://hdl.handle.net/11012/245477
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofCrystalscs
dc.relation.urihttps://www.mdpi.com/2073-4352/14/1/74cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2073-4352/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectDFT calculationsen
dc.subjectmachine learningen
dc.subjectgrain boundariesen
dc.subjectimpurity segregationen
dc.titleSegregation of Phosphorus and Silicon at the Grain Boundary in Bcc Iron via Machine-Learned Force Fieldsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-188350en
sync.item.dbtypeVAVen
sync.item.insts2024.06.27 09:46:25en
sync.item.modts2024.06.27 09:14:14en
thesis.grantorVysoké učení technické v Brně. Středoevropský technologický institut VUT. Příprava a charakterizace nanostrukturcs
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. ÚFI-odbor mikromechaniky materiálů a technické akustikycs
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ÚFI-odbor mikromechaniky materiálů a technické akustiky
Příprava a charakterizace nanostruktur