Predicting structure zone diagrams for thin film synthesis by generative machine learning

Controlling the microstructure of thin films is vital for tuning their properties. Here, machine learning is applied to obtain synthesis-composition-microstructure relationships in the form of structure zone diagrams for thin films, enabling microstructure prediction.

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Autores principales:	Lars Banko, Yury Lysogorskiy, Dario Grochla, Dennis Naujoks, Ralf Drautz, Alfred Ludwig
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492
Acceso en línea:	https://doaj.org/article/b761d7313f9244cb9fc3e14a15558671
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id	oai:doaj.org-article:b761d7313f9244cb9fc3e14a15558671
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spelling	oai:doaj.org-article:b761d7313f9244cb9fc3e14a155586712021-12-02T11:44:58ZPredicting structure zone diagrams for thin film synthesis by generative machine learning10.1038/s43246-020-0017-22662-4443https://doaj.org/article/b761d7313f9244cb9fc3e14a155586712020-03-01T00:00:00Zhttps://doi.org/10.1038/s43246-020-0017-2https://doaj.org/toc/2662-4443Controlling the microstructure of thin films is vital for tuning their properties. Here, machine learning is applied to obtain synthesis-composition-microstructure relationships in the form of structure zone diagrams for thin films, enabling microstructure prediction.Lars BankoYury LysogorskiyDario GrochlaDennis NaujoksRalf DrautzAlfred LudwigNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492ENCommunications Materials, Vol 1, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Lars Banko Yury Lysogorskiy Dario Grochla Dennis Naujoks Ralf Drautz Alfred Ludwig Predicting structure zone diagrams for thin film synthesis by generative machine learning
description	Controlling the microstructure of thin films is vital for tuning their properties. Here, machine learning is applied to obtain synthesis-composition-microstructure relationships in the form of structure zone diagrams for thin films, enabling microstructure prediction.
format	article
author	Lars Banko Yury Lysogorskiy Dario Grochla Dennis Naujoks Ralf Drautz Alfred Ludwig
author_facet	Lars Banko Yury Lysogorskiy Dario Grochla Dennis Naujoks Ralf Drautz Alfred Ludwig
author_sort	Lars Banko
title	Predicting structure zone diagrams for thin film synthesis by generative machine learning
title_short	Predicting structure zone diagrams for thin film synthesis by generative machine learning
title_full	Predicting structure zone diagrams for thin film synthesis by generative machine learning
title_fullStr	Predicting structure zone diagrams for thin film synthesis by generative machine learning
title_full_unstemmed	Predicting structure zone diagrams for thin film synthesis by generative machine learning
title_sort	predicting structure zone diagrams for thin film synthesis by generative machine learning
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/b761d7313f9244cb9fc3e14a15558671
work_keys_str_mv	AT larsbanko predictingstructurezonediagramsforthinfilmsynthesisbygenerativemachinelearning AT yurylysogorskiy predictingstructurezonediagramsforthinfilmsynthesisbygenerativemachinelearning AT dariogrochla predictingstructurezonediagramsforthinfilmsynthesisbygenerativemachinelearning AT dennisnaujoks predictingstructurezonediagramsforthinfilmsynthesisbygenerativemachinelearning AT ralfdrautz predictingstructurezonediagramsforthinfilmsynthesisbygenerativemachinelearning AT alfredludwig predictingstructurezonediagramsforthinfilmsynthesisbygenerativemachinelearning
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Predicting structure zone diagrams for thin film synthesis by generative machine learning

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