  The study of grain boundary phase transitions is an emerging field until
recently dominated by experiments. The major bottleneck in 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 new 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.
