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Friday, August 16, 2019
10:00 AM - 11:00 AM
CNLS Conference Room (TA-3, Bldg 1690)


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

Qiang Zhu
University of Nevada, Las Vegas

The phase transition of grain boundaries 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 developed a computational tool based on evolutionary algorithms that performs efficient grand-canonical grain boundary structure search and we designed a clustering analysis that automatically identifies different grain boundary phases. Its application to a model system of symmetric tilt boundaries in Cu uncovered 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 of a simple metal within the entire misorientation range have multiple phases and exhibit structural transitions. This method can be extended to study other systems (such as semiconductors and multicomponent alloys) as well.

Host: Blas Uberuaga