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.
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2020-03-01
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| Series: | Communications Materials |
| Online Access: | https://doi.org/10.1038/s43246-020-0017-2 |
| _version_ | 1818421481709764608 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T13:11:03Z |
| format | Article |
| id | doaj.art-b761d7313f9244cb9fc3e14a15558671 |
| institution | Directory Open Access Journal |
| issn | 2662-4443 |
| language | English |
| last_indexed | 2024-12-14T13:11:03Z |
| publishDate | 2020-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Materials |
| spelling | doaj.art-b761d7313f9244cb9fc3e14a155586712022-12-21T23:00:11ZengNature PortfolioCommunications 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 |
| 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 |
| title | 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_short | 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 |
| url | https://doi.org/10.1038/s43246-020-0017-2 |
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