发表机构
Interdisciplinary Centre for Advanced Materials Simulation; Ruhr University Bochum; Lawrence Livermore National Laboratory（跨学科先进材料模拟中心; 波鸿鲁尔大学; 劳伦斯利弗莫尔国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出跨模态对比学习框架，从X射线衍射图谱直接预测三维位错结构，通过共享潜在空间对齐结构-衍射表示，约500个代表性样本即可实现准确预测。
AI 中文摘要
从衍射图谱中理解和推断位错微结构仍是材料表征中的一个开放挑战，因为衍射测量仅提供关于底层位错结构的间接信息。在本工作中，开发了一种跨模态学习框架，以实现直接从衍射数据预测三维位错结构。由离散位错动力学模拟生成的位错密度场与相应的虚拟X射线衍射图谱配对，并通过对比学习嵌入到共享的二维潜在空间中。位错结构的结构表示与衍射表示之间的对齐，直接在所学潜在空间中通过相应潜在特征之间的相关性进行评估。为估计数据集大小对该方法的作用，采用最远点采样来构建具有代表性且多样化的不同大小训练子集。结果表明，跨模态对齐强，且模型性能随数据集增大而快速提升。在包含10,000个观测值的数据集中，约500个代表性观测值即可达到接近饱和的性能，从而能够从同一分布的未见衍射数据中准确预测位错密度场。定性比较证实，预测结构捕捉了底层位错微结构的主要空间特征。这些发现展示了一种学习结构-衍射关系的有效方法，并突显了直接从衍射图谱推断位错网络结构特征的潜力，为基于衍射的结构分析及未来扩展到实验数据提供了途径。
英文摘要
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.
Comments15 pages, 9 figures, preprint
