Speaker
Description
Neutron total scattering, which combining Bragg diffraction with pair distribution function (PDF) analysis, is a powerful technique for probing multiscale structural features such as short-range order, amorphous phases, nanocrystallinity, and interfaces. Advances in neutron sources and instrumentation have led to improved data processing strategies, enhancing both data quality and applicability of the technique. This work outlines current mainstream approaches for analyzing neutron total scattering data. Based on practical experience with the Multi-Physics Instrument (MPI) at the China Spallation Neutron Source (CSNS), we present optimized workflows for data preprocessing and normalization. We then introduce forward modeling techniques for structural refinement and their successful applications in both crystalline and amorphous materials. Emerging machine learning and artificial intelligence -driven methods are also discussed, with early successes in accelerating PDF fitting, generating reasonable atomic configurations, and enabling high-throughput PDF analysis using neural networks. Finally, we briefly address current challenges and ongoing efforts. This work aims to provide a methodological reference for local structure studies of complex functional materials and to support broader adoption of neutron total scattering in fields such as energy, magnetic, and catalytic materials.