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Predicting phase behavior of grain boundaries with evolutionary search and machine learning

The study of grain boundary phase transitions is an emerging field until recently dominated by experiments. The major bottleneck in the exploration of this phenomenon with atomistic modeling has been the lack of a robust computational tool that can predict interface structure. Here we develop a comp...

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Autores principales: Zhu, Qiang, Samanta, Amit, Li, Bingxi, Rudd, Robert E., Frolov, Timofey
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5794988/
https://www.ncbi.nlm.nih.gov/pubmed/29391453
http://dx.doi.org/10.1038/s41467-018-02937-2
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author Zhu, Qiang
Samanta, Amit
Li, Bingxi
Rudd, Robert E.
Frolov, Timofey
author_facet Zhu, Qiang
Samanta, Amit
Li, Bingxi
Rudd, Robert E.
Frolov, Timofey
author_sort Zhu, Qiang
collection PubMed
description The study of grain boundary phase transitions is an emerging field until recently dominated by experiments. The major bottleneck in the exploration of this phenomenon with atomistic modeling has been the lack of a robust computational tool that can predict interface structure. Here we develop a computational tool based on evolutionary algorithms that performs efficient grand-canonical grain boundary structure search and we design a clustering analysis that automatically identifies different grain boundary phases. Its application to a model system of symmetric tilt boundaries in Cu uncovers an unexpected rich polymorphism in the grain boundary structures. We find new ground and metastable states by exploring structures with different atomic densities. Our results demonstrate that the grain boundaries within the entire misorientation range have multiple phases and exhibit structural transitions, suggesting that phase behavior of interfaces is likely a general phenomenon.
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spelling pubmed-57949882018-02-05 Predicting phase behavior of grain boundaries with evolutionary search and machine learning Zhu, Qiang Samanta, Amit Li, Bingxi Rudd, Robert E. Frolov, Timofey Nat Commun Article The study of grain boundary phase transitions is an emerging field until recently dominated by experiments. The major bottleneck in the exploration of this phenomenon with atomistic modeling has been the lack of a robust computational tool that can predict interface structure. Here we develop a computational tool based on evolutionary algorithms that performs efficient grand-canonical grain boundary structure search and we design a clustering analysis that automatically identifies different grain boundary phases. Its application to a model system of symmetric tilt boundaries in Cu uncovers an unexpected rich polymorphism in the grain boundary structures. We find new ground and metastable states by exploring structures with different atomic densities. Our results demonstrate that the grain boundaries within the entire misorientation range have multiple phases and exhibit structural transitions, suggesting that phase behavior of interfaces is likely a general phenomenon. Nature Publishing Group UK 2018-02-01 /pmc/articles/PMC5794988/ /pubmed/29391453 http://dx.doi.org/10.1038/s41467-018-02937-2 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Zhu, Qiang
Samanta, Amit
Li, Bingxi
Rudd, Robert E.
Frolov, Timofey
Predicting phase behavior of grain boundaries with evolutionary search and machine learning
title Predicting phase behavior of grain boundaries with evolutionary search and machine learning
title_full Predicting phase behavior of grain boundaries with evolutionary search and machine learning
title_fullStr Predicting phase behavior of grain boundaries with evolutionary search and machine learning
title_full_unstemmed Predicting phase behavior of grain boundaries with evolutionary search and machine learning
title_short Predicting phase behavior of grain boundaries with evolutionary search and machine learning
title_sort predicting phase behavior of grain boundaries with evolutionary search and machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5794988/
https://www.ncbi.nlm.nih.gov/pubmed/29391453
http://dx.doi.org/10.1038/s41467-018-02937-2
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