Foundation Model for Polycrystalline Material Informatics

We present a three-dimensional foundation model for polycrystalline materials based on a masked autoencoder trained via large-scale self-supervised learning. The model is pretrained on $\mathrm{100,000}$ voxelized synthetic face-centered cubic (FCC) microstructures whose crystallographic textures systematically span the texture hull using hierarchical simplex sampling. The transferability of the learned latent representations is evaluated on two downstream tasks: homogenized elastic stiffness prediction and nonlinear stress-strain response prediction. For the nonlinear task, the pretrained encoder is coupled with an orientation-aware interaction-based deep material network (ODMN), where latent features are used to infer microstructure-dependent surrogate parameters. The inferred ODMNs are subsequently combined with crystal plasticity to predict stress–strain responses for previously unseen microstructures. In stiffness prediction, the pretrained model achieves validation $R^2$ values exceeding 0.8, compared to below 0.1 for non-pretrained baselines. In nonlinear response prediction, mean stress errors remain below 4%. These results demonstrate that self-supervised pretraining yields physically meaningful and transferable microstructural representations, providing a scalable framework for microstructure-property inference in polycrystalline materials.