Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-11395
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dc.contributor.authorHuber, Liam-
dc.contributor.authorHadian, Raheleh-
dc.contributor.authorGrabowski, Blazej-
dc.contributor.authorNeugebauer, Jörg-
dc.date.accessioned2021-04-07T10:28:45Z-
dc.date.available2021-04-07T10:28:45Z-
dc.date.issued2018de
dc.identifier.isbn2057-3960-
dc.identifier.urihttp://elib.uni-stuttgart.de/handle/11682/11412-
dc.identifier.urihttp://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-114127de
dc.identifier.urihttp://dx.doi.org/10.18419/opus-11395-
dc.description.abstractEven minute amounts of one solute atom per one million bulk atoms may give rise to qualitative changes in the mechanical response and fracture resistance of modern structural materials. These changes are commonly related to enrichment by several orders of magnitude of the solutes at structural defects in the host lattice. The underlying concept - segregation - is thus fundamental in materials science. To include it in modern strategies of materials design, accurate and realistic computational modelling tools are necessary. However, the enormous number of defect configurations as well as sites solutes can occupy requires models which rely on severe approximations. In the present study we combine a high-throughput study containing more than 1 million data points with machine learning to derive a computationally highly efficient framework which opens the opportunity to model this important mechanism on a routine basis.en
dc.language.isoende
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/639211de
dc.relation.uridoi:10.1038/s41524-018-0122-7de
dc.rightsinfo:eu-repo/semantics/openAccessde
dc.subject.ddc530de
dc.titleA machine learning approach to model solute grain boundary segregationen
dc.typearticlede
ubs.fakultaetChemiede
ubs.fakultaetFakultätsübergreifend / Sonstige Einrichtungde
ubs.institutInstitut für Materialwissenschaftde
ubs.institutFakultätsübergreifend / Sonstige Einrichtungde
ubs.publikation.noppnyesde
ubs.publikation.seiten8de
ubs.publikation.sourcenpj computational materials, 4 (2018), 64de
ubs.publikation.typZeitschriftenartikelde
Appears in Collections:03 Fakultät Chemie

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