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Machine‐Learning Microstructure for Inverse Material Design
Metallurgy and material design have thousands of years’ history and have played a critical role in the civilization process of humankind. The traditional trial‐and‐error method has been unprecedentedly challenged in the modern era when the number of components and phases in novel alloys keeps increa...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8655171/ https://www.ncbi.nlm.nih.gov/pubmed/34716677 http://dx.doi.org/10.1002/advs.202101207 |
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author | Pei, Zongrui Rozman, Kyle A. Doğan, Ömer N. Wen, Youhai Gao, Nan Holm, Elizabeth A. Hawk, Jeffrey A. Alman, David E. Gao, Michael C. |
author_facet | Pei, Zongrui Rozman, Kyle A. Doğan, Ömer N. Wen, Youhai Gao, Nan Holm, Elizabeth A. Hawk, Jeffrey A. Alman, David E. Gao, Michael C. |
author_sort | Pei, Zongrui |
collection | PubMed |
description | Metallurgy and material design have thousands of years’ history and have played a critical role in the civilization process of humankind. The traditional trial‐and‐error method has been unprecedentedly challenged in the modern era when the number of components and phases in novel alloys keeps increasing, with high‐entropy alloys as the representative. New opportunities emerge for alloy design in the artificial intelligence era. Here, a successful machine‐learning (ML) method has been developed to identify the microstructure images with eye‐challenging morphology for a number of martensitic and ferritic steels. Assisted by it, a new neural‐network method is proposed for the inverse design of alloys with 20 components, which can accelerate the design process based on microstructure. The method is also readily applied to other material systems given sufficient microstructure images. This work lays the foundation for inverse alloy design based on microstructure images with extremely similar features. |
format | Online Article Text |
id | pubmed-8655171 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86551712021-12-20 Machine‐Learning Microstructure for Inverse Material Design Pei, Zongrui Rozman, Kyle A. Doğan, Ömer N. Wen, Youhai Gao, Nan Holm, Elizabeth A. Hawk, Jeffrey A. Alman, David E. Gao, Michael C. Adv Sci (Weinh) Research Articles Metallurgy and material design have thousands of years’ history and have played a critical role in the civilization process of humankind. The traditional trial‐and‐error method has been unprecedentedly challenged in the modern era when the number of components and phases in novel alloys keeps increasing, with high‐entropy alloys as the representative. New opportunities emerge for alloy design in the artificial intelligence era. Here, a successful machine‐learning (ML) method has been developed to identify the microstructure images with eye‐challenging morphology for a number of martensitic and ferritic steels. Assisted by it, a new neural‐network method is proposed for the inverse design of alloys with 20 components, which can accelerate the design process based on microstructure. The method is also readily applied to other material systems given sufficient microstructure images. This work lays the foundation for inverse alloy design based on microstructure images with extremely similar features. John Wiley and Sons Inc. 2021-10-29 /pmc/articles/PMC8655171/ /pubmed/34716677 http://dx.doi.org/10.1002/advs.202101207 Text en © 2021 The Authors. Advanced Science published by Wiley‐VCH GmbH https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Pei, Zongrui Rozman, Kyle A. Doğan, Ömer N. Wen, Youhai Gao, Nan Holm, Elizabeth A. Hawk, Jeffrey A. Alman, David E. Gao, Michael C. Machine‐Learning Microstructure for Inverse Material Design |
title | Machine‐Learning Microstructure for Inverse Material Design |
title_full | Machine‐Learning Microstructure for Inverse Material Design |
title_fullStr | Machine‐Learning Microstructure for Inverse Material Design |
title_full_unstemmed | Machine‐Learning Microstructure for Inverse Material Design |
title_short | Machine‐Learning Microstructure for Inverse Material Design |
title_sort | machine‐learning microstructure for inverse material design |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8655171/ https://www.ncbi.nlm.nih.gov/pubmed/34716677 http://dx.doi.org/10.1002/advs.202101207 |
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