Predicting phase behavior of grain boundaries with evolutionary search and machine learning
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.
- Authors:
-
[1];
[3];
- Univ. of Nevada, Las Vegas, NV (United States)
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Univ. of California, Davis, CA (United States)
- Publication Date:
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1466176
- Report Number(s):
- LLNL-JRNL-735504
Journal ID: ISSN 2041-1723; 887780
- Grant/Contract Number:
- AC52-07NA27344; NA0001982
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Communications
- Additional Journal Information:
- Journal Volume: 9; Journal Issue: 1; Journal ID: ISSN 2041-1723
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; 97 MATHEMATICS AND COMPUTING
Citation Formats
Zhu, Qiang, Samanta, Amit, Li, Bingxi, Rudd, Robert E., and Frolov, Timofey. Predicting phase behavior of grain boundaries with evolutionary search and machine learning. United States: N. p., 2018.
Web. doi:10.1038/s41467-018-02937-2.
Zhu, Qiang, Samanta, Amit, Li, Bingxi, Rudd, Robert E., & Frolov, Timofey. Predicting phase behavior of grain boundaries with evolutionary search and machine learning. United States. https://doi.org/10.1038/s41467-018-02937-2
Zhu, Qiang, Samanta, Amit, Li, Bingxi, Rudd, Robert E., and Frolov, Timofey. Thu .
"Predicting phase behavior of grain boundaries with evolutionary search and machine learning". United States. https://doi.org/10.1038/s41467-018-02937-2. https://www.osti.gov/servlets/purl/1466176.
@article{osti_1466176,
title = {Predicting phase behavior of grain boundaries with evolutionary search and machine learning},
author = {Zhu, Qiang and Samanta, Amit and Li, Bingxi and Rudd, Robert E. and Frolov, Timofey},
abstractNote = {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.},
doi = {10.1038/s41467-018-02937-2},
journal = {Nature Communications},
number = 1,
volume = 9,
place = {United States},
year = {Thu Feb 01 00:00:00 EST 2018},
month = {Thu Feb 01 00:00:00 EST 2018}
}
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