Powder X Ray Diffraction

Powder X-ray diffraction (PXRD) is a crucial technique for determining the crystal structure of materials, but analyzing PXRD data, especially from complex or imperfect samples, remains challenging. Current research focuses on developing machine learning models, including deep generative models (like diffusion models and U-Nets) and other supervised learning algorithms, to automate and improve the accuracy of phase identification, quantification, and even *ab initio* structure solution from PXRD data, even with limited or noisy input. These advancements are significantly impacting materials science by accelerating materials discovery and characterization, enabling more efficient analysis of complex samples, and facilitating automation of laboratory workflows.