Document Type
Article
Publication Date
2-1-2018
Publication Title
Nature Communications
Publisher
Nature Publishing Group
Volume
9
Issue
1
First page number:
1
Last page number:
9
Abstract
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. © 2018 The Author(s).
File Format
File Size
2000 KB
Language
English
Rights
IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Repository Citation
Zhu, Q.,
Samanta, A.,
Li, B.,
Rudd, R. E.,
Frolov, T.
(2018).
Predicting Phase Behavior Of Grain Boundaries With Evolutionary Search And Machine Learning.
Nature Communications, 9(1),
1-9.
Nature Publishing Group.
http://dx.doi.org/10.1038/s41467-018-02937-2