  We develop a polycrystal graph neural network (PGNN) model for predicting the
effective properties of polycrystalline materials, using the Li7La3Zr2O12
ceramic as an example. A large-scale dataset with >5000 different
three-dimensional polycrystalline microstructures of finite-width grain
boundary is generated by Voronoi tessellation and processing of the electron
backscatter diffraction images. The effective ion conductivities and elastic
stiffness coefficients of these microstructures are calculated by
high-throughput physics-based simulations. The optimized PGNN model achieves a
low error of <1.4% in predicting all three diagonal components of the effective
Li-ion conductivity matrix, outperforming a linear regression model and two
baseline convolutional neural network models. Sequential forward selection
method is used to quantify the relative importance of selecting individual
grain (boundary) features to improving the property prediction accuracy,
through which both the critical and unwanted node (edge) feature can be
determined. The extrapolation performance of the trained PGNN model is also
investigated. The transfer learning performance is evaluated by using the PGNN
model pretrained for predicting conductivities to predict the elastic
properties of the same set of microstructures.
