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

Autor: Alexandra M. Goryaeva, Clovis Lapointe, Chendi Dai, Julien Dérès, Jean-Bernard Maillet, Mihai-Cosmin Marinica

Publikováno v: Nature Communications, Vol 11, Iss 1, Pp 1-14 (2020)

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 a

Externí odkaz: https://doaj.org/article/9c4d246c54c44202a50b99c9a8ea072c

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

Autor: Chendi Dai, Mihai-Cosmin Marinica, Alexandra Goryaeva, Clovis Lapointe, Jean-Bernard Maillet, Julien Dérès

Publikováno v: Nature Communications
Nature Communications, Vol 11, Iss 1, Pp 1-14 (2020)
Nature Communications, 2020, 11 (1), pp.4691. ⟨10.1038/s41467-020-18282-2⟩

This work revises the concept of defects in crystalline solids and proposes a universal strategy for their characterization at the atomic scale using outlier detection based on statistical distances. The proposed strategy provides a generic measure t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f4d7d3cf4e3532caa26cf2f2fa0fc908