WSTHU4: Machine Learning Augmented X-Ray Scattering and Spectroscopies

Virtual Workshop (Zoom Link:

Virtual Workshop

Zoom Link:


Synchrotron-based X-ray scattering and spectroscopies are the state-of-the-art materials characterization method that measures materials’ structural and dynamical properties with an unprecedented level of precision. It plays a critical roles in a large variety of materials from catalysis to bio- macromolecules, from energy materials to quantum materials. In recent years, X-ray scattering has made significant progress thanks to the higher photon flux and multimodal measurements, which increases the data volume and data dimension. To address the associated challenge on data analysis, the X-ray scattering has also been augmented by computational modeling and the rapid implementation of machine learning. In light of this, we are proposing this timely Workshop to introduce the recent progresses how machine learning can benefit various perspectives of X-ray scattering and spectroscopies. We divide the Workshop into several sub-fields, including structural characterization from atomic to mesoscopic scales, dynamical and spectroscopic properties, and beam optimization and uni- and multimodal data analysis. In both structural and dynamical properties, we will balance the speakers from soft materials in chemistry and biology with speakers working on hard condensed matters. By focusing on a few emerging architectures of machine learning as well as highlighting the pressing questions that can be answered by machine learning, our proposed Workshop can tremendously benefit NSLS-II and CFN by bringing in potential new deployment on hardware and analysis augmentation, as well as new research opportunities. The workshop will bring together scientists in the field of X-ray scattering, materials informatics, and data science to seek for the viable pathways to augment NSLS-II beamlines to explore materials properties.

Workshop Organizers:
Mingda Li (MIT)
Deyu Lu (BNL)
Eli Stavitski (BNL)
Xiaohui Qu (BNL)

Back to main agenda

    • 09:00 09:45
      Keeping up with the data: accelerating discovery in a high data velocity world 45m
      Speaker: Simon Billinge (Columbia University)
    • 09:45 10:15
      Towards a Deep Neural Network for General Structure-Property Mapping of Complex Systems 30m
      Speaker: Tom Penfold (Newcastle University)
    • 10:15 10:45
      First principles near-edge X-ray absorption spectra database development: Workflow, validation and benchmarking 30m
      Speaker: John Vinson (The National Institute of Standards and Technology)
    • 10:45 10:55
      Break 10m
    • 10:55 11:25
      Classifying and predicting x-ray absorption spectra using machine learning 30m
      Speaker: Matthew Carbone (Columbia University)
    • 11:25 11:55
      Latent Space Analysis of Spectra 30m
      Speaker: Anatoly Frenkel (Stony Brook University)
    • 11:55 13:00
      Break 1h 5m
    • 13:00 13:45
      Integrating Theory with Machine Learning in Accelerating Materials Characterization 45m
      Speaker: Maria Chan (Argonne National Laboratory)
    • 13:45 14:15
      TBD 30m
      Speaker: Alex Hexemer (Lawrence Berkeley National Laboratory)
    • 14:15 14:45
      Bypassing Quantum Mechanics for Material Science with Machine Learning 30m
      Speaker: Emine Kucukbenli (Boston University)
    • 14:45 14:55
      Break 10m
    • 14:55 15:25
      Time-resolved RIXS theory and applications in quantum materials 30m
      Speaker: Yao Wang (Clemson University)
    • 15:25 15:55
      Analysis of Synchrotron Extended X-ray Absorption Fine Structure (EXAFS) Data Using Artificial Intelligence Techniques 30m
      Speaker: Jeff Terry (Illinois Institute of Technology)
    • 15:55 16:25
      Artificial neural networks inverting 2p XAS spectra 30m
      Speaker: Johann Luder (National Sun Yat-sen University)