Diffraction Pattern

Diffraction patterns, the spatial distribution of scattered waves, are crucial for determining the structure of materials and environments across diverse scales, from atomic arrangements to macroscopic landscapes. Current research heavily utilizes machine learning, particularly deep convolutional neural networks, to address challenges like phase retrieval from incomplete or noisy data, accelerating computationally intensive algorithms (e.g., for ptychography and Laue diffraction), and improving the accuracy of pattern classification and reconstruction. These advancements are significantly impacting fields ranging from materials science and structural biology (via X-ray free-electron lasers) to wireless communication and microscopy, enabling faster, more accurate, and higher-resolution imaging and analysis.