Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores

The presence of defects in crystalline solids affects material properties, the precise knowledge of defect characteristics being highly desirable. Here the authors demonstrate a machine-learning outlier detection method based on distortion score as an effective tool for modelling defects in crystall...

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Autores principales: Alexandra M. Goryaeva, Clovis Lapointe, Chendi Dai, Julien Dérès, Jean-Bernard Maillet, Mihai-Cosmin Marinica
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Lenguaje:EN
Publicado: Nature Portfolio 2020
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Acceso en línea:https://doaj.org/article/9c4d246c54c44202a50b99c9a8ea072c
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spelling oai:doaj.org-article:9c4d246c54c44202a50b99c9a8ea072c2021-12-02T17:24:13ZReinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores10.1038/s41467-020-18282-22041-1723https://doaj.org/article/9c4d246c54c44202a50b99c9a8ea072c2020-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-18282-2https://doaj.org/toc/2041-1723The presence of defects in crystalline solids affects material properties, the precise knowledge of defect characteristics being highly desirable. Here the authors demonstrate a machine-learning outlier detection method based on distortion score as an effective tool for modelling defects in crystalline solids.Alexandra M. GoryaevaClovis LapointeChendi DaiJulien DérèsJean-Bernard MailletMihai-Cosmin MarinicaNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-14 (2020)
institution DOAJ
collection DOAJ
language EN
topic Science
Q
spellingShingle Science
Q
Alexandra M. Goryaeva
Clovis Lapointe
Chendi Dai
Julien Dérès
Jean-Bernard Maillet
Mihai-Cosmin Marinica
Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores
description The presence of defects in crystalline solids affects material properties, the precise knowledge of defect characteristics being highly desirable. Here the authors demonstrate a machine-learning outlier detection method based on distortion score as an effective tool for modelling defects in crystalline solids.
format article
author Alexandra M. Goryaeva
Clovis Lapointe
Chendi Dai
Julien Dérès
Jean-Bernard Maillet
Mihai-Cosmin Marinica
author_facet Alexandra M. Goryaeva
Clovis Lapointe
Chendi Dai
Julien Dérès
Jean-Bernard Maillet
Mihai-Cosmin Marinica
author_sort Alexandra M. Goryaeva
title Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores
title_short Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores
title_full Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores
title_fullStr Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores
title_full_unstemmed Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores
title_sort reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores
publisher Nature Portfolio
publishDate 2020
url https://doaj.org/article/9c4d246c54c44202a50b99c9a8ea072c
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