Foundation Model for Polycrystalline Material Informatics
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912784785080320 |
|---|---|
| author | Wei, Ting-Ju Chen, Chuin-Shan |
| author_facet | Wei, Ting-Ju Chen, Chuin-Shan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06770 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Foundation Model for Polycrystalline Material Informatics Wei, Ting-Ju Chen, Chuin-Shan Computational Engineering, Finance, and Science 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. |
| title | Foundation Model for Polycrystalline Material Informatics |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2512.06770 |