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

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.

Bibliographic Details
Main Authors: Lars Banko, Yury Lysogorskiy, Dario Grochla, Dennis Naujoks, Ralf Drautz, Alfred Ludwig
Format: Article
Language:English
Published: Nature Publishing Group 2020-03-01
Series:Communications Materials
Online Access:https://doi.org/10.1038/s43246-020-0017-2
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spelling doaj-b761d7313f9244cb9fc3e14a155586712021-03-28T11:17:39ZengNature Publishing GroupCommunications Materials2662-44432020-03-011111010.1038/s43246-020-0017-2Predicting structure zone diagrams for thin film synthesis by generative machine learningLars Banko0Yury Lysogorskiy1Dario Grochla2Dennis Naujoks3Ralf Drautz4Alfred Ludwig5Chair for Materials Discovery and Interfaces, Institute for Materials, Ruhr-UniversitätInterdisciplinary Centre for Advanced Materials Simulation (ICAMS), Ruhr-UniversitätChair for Materials Discovery and Interfaces, Institute for Materials, Ruhr-UniversitätChair for Materials Discovery and Interfaces, Institute for Materials, Ruhr-UniversitätInterdisciplinary Centre for Advanced Materials Simulation (ICAMS), Ruhr-UniversitätChair for Materials Discovery and Interfaces, Institute for Materials, Ruhr-UniversitätControlling 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.https://doi.org/10.1038/s43246-020-0017-2
collection DOAJ
language English
format Article
sources DOAJ
author Lars Banko
Yury Lysogorskiy
Dario Grochla
Dennis Naujoks
Ralf Drautz
Alfred Ludwig
spellingShingle Lars Banko
Yury Lysogorskiy
Dario Grochla
Dennis Naujoks
Ralf Drautz
Alfred Ludwig
Predicting structure zone diagrams for thin film synthesis by generative machine learning
Communications Materials
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 Publishing Group
series Communications Materials
issn 2662-4443
publishDate 2020-03-01
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.
url https://doi.org/10.1038/s43246-020-0017-2
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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