Interpreting X-ray Diffraction Patterns of Metal-Organic Frameworks via Generative Artificial Intelligence.

Journal: Journal of the American Chemical Society
Published Date: Dec 20, 2025

Abstract

Metal-organic frameworks (MOFs) have attracted considerable attention owing to their multifaceted applications and structurally tunable characteristics. Powder X-ray diffraction (XRD) is an essential technique for high-throughput characterization of MOFs. However, it remains challenging to automatically interpret the XRD data due to the diversity and complexity of the geometric structures of MOFs. Herein, we propose a generative artificial intelligence framework based on the Stable-Diffusion architecture for deciphering the structures of MOFs from powder XRD patterns. This model, named as Xrd2Mof, has incorporated domain-specific knowledge by using a coarse-grained representation scheme, which leads to an accuracy of over 93% in identifying the ground truth MOF structure corresponding to the targeted XRD pattern. Xrd2Mof can be directly applied to a diverse range of MOF structures that cover nearly all types of framework topologies, thereby establishing a novel technological avenue for automated structural analysis of MOFs in self-driving laboratories.

Authors

  • Bin Feng
    Department of Orthopedic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
  • Bingxu Wang
    School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, P.R. China.
  • Linpeng Lv
    School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, P.R. China.
  • Mingzheng Zhang
    School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, P. R. China.
  • Zhefeng Chen
    School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, P. R. China.
  • Feng Pan
    Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China.
  • Shunning Li
    School of Energy and Environment, City University of Hong Kong, Kowloon, Hong Kong 999077, P.R. China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals