A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses

A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses

Understanding plastic deformation in metallic glasses is challenging due to their heterogeneous atomic environments. Here the authors propose a machine learning approach generalizable across compositions to predict the structural features from which plastic deformation is initiated in a metallic gla...

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Autores principales: Qi Wang, Anubhav Jain
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2019
Materias:
Science
Q
Acceso en línea:https://doaj.org/article/444c69cac40f4b0eabb51d733ddb8f58
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spelling oai:doaj.org-article:444c69cac40f4b0eabb51d733ddb8f582021-12-02T16:57:11ZA transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses10.1038/s41467-019-13511-92041-1723https://doaj.org/article/444c69cac40f4b0eabb51d733ddb8f582019-12-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-13511-9https://doaj.org/toc/2041-1723Understanding plastic deformation in metallic glasses is challenging due to their heterogeneous atomic environments. Here the authors propose a machine learning approach generalizable across compositions to predict the structural features from which plastic deformation is initiated in a metallic glass.Qi WangAnubhav JainNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-11 (2019)
institution DOAJ
collection DOAJ
language EN
topic Science
Q
spellingShingle Science
Q
Qi Wang
Anubhav Jain
A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses
description Understanding plastic deformation in metallic glasses is challenging due to their heterogeneous atomic environments. Here the authors propose a machine learning approach generalizable across compositions to predict the structural features from which plastic deformation is initiated in a metallic glass.
format article
author Qi Wang
Anubhav Jain
author_facet Qi Wang
Anubhav Jain
author_sort Qi Wang
title A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses
title_short A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses
title_full A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses
title_fullStr A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses
title_full_unstemmed A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses
title_sort transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses
publisher Nature Portfolio
publishDate 2019
url https://doaj.org/article/444c69cac40f4b0eabb51d733ddb8f58
work_keys_str_mv AT qiwang atransferablemachinelearningframeworklinkingintersticedistributionandplasticheterogeneityinmetallicglasses
AT anubhavjain atransferablemachinelearningframeworklinkingintersticedistributionandplasticheterogeneityinmetallicglasses
AT qiwang transferablemachinelearningframeworklinkingintersticedistributionandplasticheterogeneityinmetallicglasses
AT anubhavjain transferablemachinelearningframeworklinkingintersticedistributionandplasticheterogeneityinmetallicglasses
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