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

Abstract

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.