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This study applies cross-modal contrastive learning to reconstruct 3D dislocation microstructures directly from X-ray diffraction data, addressing a key inverse problem in materials characterization. By embedding structural simulations and diffraction patterns into a shared latent space, the model achieves high accuracy with limited training samples, demonstrating the viability of AI-driven inference for indirect material measurements. The work highlights how machine learning can bridge the gap between indirect experimental signals and complex physical microstructures without requiring full simulation runs.
Inferring Dislocation Microstructures from X-ray Diffraction via Cross-Modal Contrastive Learning
arXiv:2609.12713v1 Announce Type: cross Abstract: 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.
Abstract: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.
Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
arXiv:2609.12713 [cond-mat.mtrl-sci]
(or arXiv:2609.12713v1 [cond-mat.mtrl-sci] for this version)
https://doi.org/10.48550/arXiv.2609.12713 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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