Comparing Structural Representations of Grain Boundaries

The properties of polycrystalline materials are are a function of microstructure. The property-microstructure relationship has motivated many physics-inspired structural representations of materials. Although mainly used to train accurate inexpensive interatomic potentials by means of machine learning, these representations accurately represent some grain boundary properties. Using a database of over 7000 grain boundaries, we evaluate different representations and their abilities to express the relevant structural information. We predict grain boundary energy with each representation in a machine-learned model. Our comparison identifies promising grain boundary descriptors.