Qiang Zhu published an article, Predicting Phase Behavior of Grain Boundaries with Evolutionary Search and Machine Learning, in Nature Communications. The study of grain boundary phase transitions is an emerging field until recently dominated by experiments. Zhu, along with collaborators at Lawrence Livermore National Laboratory, developed a computational tool based on evolutionary algorithms that performs efficient grand-canonical grain boundary structure search. Its application to a model system of symmetric tilt boundaries in Cu uncovers an unexpected rich polymorphism in the grain boundary structures. The 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.
