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 $100{,}000$ 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.

翻译：我们提出了一种基于大规模自监督训练掩码自编码器的三维多晶材料基础模型。该模型在100,000个体素化的合成面心立方（FCC）微观结构上进行预训练，这些结构通过分层单纯形采样系统性地覆盖了织构空间的完整边界。学习到的潜在表征的可迁移性在两个下游任务中进行了评估：均匀化弹性刚度预测和非线性应力-应变响应预测。对于非线性任务，预训练编码器与基于取向感知交互的深度材料网络（ODMN）耦合，其中潜在特征用于推断微观结构相关的代理参数。推断出的ODMN随后与晶体塑性理论相结合，以预测未见微观结构的应力-应变响应。在刚度预测任务中，预训练模型的验证R²值超过0.8，而未预训练的基线模型则低于0.1。在非线性响应预测中，平均应力误差保持在4%以下。这些结果表明，自监督预训练能够产生具有物理意义且可迁移的微观结构表征，为多晶材料的微观结构-性能推断提供了一个可扩展的框架。