We present a three-dimensional polycrystal foundation model based on a masked autoencoder that learns intrinsic microstructural representations through large-scale self-supervised pretraining on voxel-based data. The pretraining dataset consists of 100,000 face-centered cubic (FCC) microstructures whose crystallographic textures span the texture hull via hierarchical simplex sampling. The quality and transferability of the learned representations are evaluated through two downstream tasks: (i) homogenized stiffness prediction and (ii) nonlinear homogenized response prediction. In the latter, the pretrained encoder is coupled with an orientation-aware interaction-based deep material network (ODMN), where the learned latent representations are used to infer microstructure-dependent ODMN parameters. This enables accurate stress-strain predictions for previously unseen microstructures under crystal plasticity. Across both tasks, the pretrained encoder consistently exhibits superior generalization performance compared to non-pretrained baselines. These results demonstrate the strong transferability of the proposed foundation model and its effectiveness in data-scarce scientific settings with limited labeled microstructures. The framework further enables scalable integration with experimentally derived microstructures, providing a practical basis for microstructure-property reasoning in materials design.
Wei et al. (Sun,) studied this question.