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 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. The atomic structure of grain boundary phases remains unknown and is difficult to investigate experimentally. Here, the authors use an evolutionary algorithm to computationally explore interface structures in higher dimensions and predict low-energy configurations, showing interface phases may be ubiquitous.