📝 SummarXiv

Abstract

Dislocations control the mechanical behavior of crystalline materials, yet their quantitative characterization in bulk has remained elusive. Transmission Electron Microscopy provides atomic-scale resolution but is restricted to thin foils, limiting relevance to structural performance. Dark-field X-ray microscopy (DFXM) has recently opened access to three-dimensional, non-destructive imaging of dislocations in macroscopic crystals. A critical bottleneck, however, is the reliable identification of weak- versus strong-beam conditions. Weak-beam imaging enhances dislocation contrast, while strong-beam conditions are dominated by multiple scattering and obscure interpretation. Current practice depends on manual classification by specialists, which is subjective, slow, and incompatible with the scale of modern experiments. Here, we introduce a deep learning framework that automates this task using a lightweight convolutional neural network trained on small, hand-labeled datasets. By enabling robust, rapid, and scalable identification of imaging conditions, this approach transforms DFXM into a high-throughput tool, unlocking statistically significant studies of dislocation dynamics in bulk materials.