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Inferring Dislocation Microstructures from X-ray Diffraction via Cross-Modal Contrastive Learning — arXiv:2609.12713

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.12713
License (as archived): CC0-1.0
Archived: 2026-10-16T03:16:08.422Z
SHA-256 of the archived text: 0cb1268ce7487037c4dbe251001e125f81df627a34f915e0b787dbf1891c587a
Stable URL of this page: https://metraipolis.com/documento/dc_79bbb6092452

How to cite

Inferring Dislocation Microstructures from X-ray Diffraction via Cross-Modal Contrastive Learning — arXiv:2609.12713 — arXiv (en), https://arxiv.org/abs/2609.12713 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_79bbb6092452 (Metraipolis archive dc_79bbb6092452, sha256 0cb1268ce7487037c4dbe251001e125f81df627a34f915e0b787dbf1891c587a)

BibTeX

@misc{dc79bbb6092452,
  title = {Inferring Dislocation Microstructures from X-ray Diffraction via Cross-Modal Contrastive Learning — arXiv:2609.12713},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.12713},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_79bbb6092452; sha256 0cb1268ce7487037c4dbe251001e125f81df627a34f915e0b787dbf1891c587a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.12713. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Understanding and inferring dislocation microstructures from diffraction patterns remains an open challenge in materials characterization, as diffraction measurements provide only indirect information about the underlying dislocation structure. In this work, a cross-modal learning framework is developed to enable the prediction of 3D dislocation structures directly from diffraction data. Dislocation density fields generated from discrete dislocation dynamics simulations are paired with corresponding virtual X-ray diffraction patterns and embedded into a shared 2D latent space using contrastive learning. The alignment between structural and diffraction representations of dislocation structures is evaluated directly in the learned latent space using correlations between corresponding latent features. To estimate the role of dataset size for this approach, farthest point sampling is employed to construct representative and diverse training subsets of varying sizes. The results show strong cross-modal alignment and that model performance improves rapidly with increasing dataset size. Near-saturation is achieved with approximately 500 representative observations from a dataset of 10,000 observations, enabling accurate prediction of dislocation density fields from previously unseen diffraction data of the same distribution. Qualitative comparisons confirm that the predicted structures capture the dominant spatial features of the underlying dislocation microstructures. These findings demonstrate an efficient approach for learning structure-diffraction relationships and highlight the potential for inferring structural characteristics of dislocation networks directly from diffraction patterns, providing a pathway toward diffraction-based structural analysis and future extension to experimental data.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-14 in cs.LG).
Categorie arXiv: Materials Science (cond-mat.mtrl-sci) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.12713 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T03:05:44.112Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-14?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).