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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Auteurs principaux:	Alexandra M. Goryaeva, Clovis Lapointe, Chendi Dai, Julien Dérès, Jean-Bernard Maillet, Mihai-Cosmin Marinica
Format:	article
Langue:	EN
Publié:	Nature Portfolio 2020
Sujets:	Science Q
Accès en ligne:	https://doaj.org/article/9c4d246c54c44202a50b99c9a8ea072c
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id	oai: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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Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores

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