  Various machine learning models have been used to predict the properties of
polycrystalline materials, but none of them directly consider the physical
interactions among neighboring grains despite such microscopic interactions
critically determining macroscopic material properties. Here, we develop a
graph neural network (GNN) model for obtaining an embedding of polycrystalline
microstructure which incorporates not only the physical features of individual
grains but also their interactions. The embedding is then linked to the target
property using a feed-forward neural network. Using the magnetostriction of
polycrystalline Tb0.3Dy0.7Fe2 alloys as an example, we show that a single GNN
model with fixed network architecture and hyperparameters allows for a low
prediction error of ~10% over a group of remarkably different microstructures
as well as quantifying the importance of each feature in each grain of a
microstructure to its magnetostriction. Such microstructure-graph-based GNN
model therefore enables an accurate and interpretable prediction of the
properties of polycrystalline materials.

Materials Science