Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-11395
Authors: Huber, Liam
Hadian, Raheleh
Grabowski, Blazej
Neugebauer, Jörg
Title: A machine learning approach to model solute grain boundary segregation
Issue Date: 2018
metadata.ubs.publikation.typ: Zeitschriftenartikel
metadata.ubs.publikation.seiten: 8
metadata.ubs.publikation.source: npj computational materials, 4 (2018), 64
URI: http://elib.uni-stuttgart.de/handle/11682/11412
http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-114127
http://dx.doi.org/10.18419/opus-11395
ISBN: 2057-3960
Abstract: Even 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.
Appears in Collections:03 Fakultät Chemie

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