Metal-organic frameworks (MOFs) have attracted significant interest within the scientific community, primarily due to their customizable structures and versatile applications. While X-ray diffraction technology is a well-established tool for material characterization, efficiently analyzing powder X-ray diffraction (PXRD) data and predicting the crystal structures of MOF materials in high-throughput experimental and self-driving laboratory settings continues to pose challenges for researchers. Professor Pan Feng's team, based at the School of Advanced Materials at Peking University's Shenzhen Graduate School, specializes in graph-theoretic structural chemistry, AI for Science (AI4S), and materials genomics. They have successfully leveraged artificial intelligence technology to analyze XRD data and have innovatively developed a generative artificial intelligence framework, Xrd2Mof, utilizing a diffusion model. This framework utilizes PXRD patterns, metal nodes, and organic ligand information as inputs, directly outputting MOF structures and thus achieving a significant technological breakthrough.