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  A machine learning approach to model solute grain boundary segregation

Huber, L., Hadian, R., Grabowski, B., & Neugebauer, J. (2018). A machine learning approach to model solute grain boundary segregation. npj Computational Materials, 4(1): 64. doi:10.1038/s41524-018-0122-7.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-A390-1 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-569C-B
Genre: Journal Article

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 Creators:
Huber, Liam1, Author              
Hadian, Raheleh1, Author              
Grabowski, Blazej1, Author              
Neugebauer, Jörg2, Author              
Affiliations:
1Adaptive Structural Materials (Simulation), Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863339              
2Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863337              

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 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. © 2018, The Author(s).

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Language(s): eng - English
 Dates: 2018-12-01
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1038/s41524-018-0122-7
BibTex Citekey: Huber2018
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Title: npj Computational Materials
  Abbreviation : npj Comput. Mater.
Source Genre: Journal
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Publ. Info: London : Springer Nature
Pages: - Volume / Issue: 4 (1) Sequence Number: 64 Start / End Page: - Identifier: ISSN: 2057-3960
CoNE: https://pure.mpg.de/cone/journals/resource/2057-3960