WSTHU4: Machine Learning Augmented X-Ray Scattering and Spectroscopies

Thursday 20 May 2021, 09:00 → 16:25 US/Eastern

Virtual Workshop (Zoom Link: https://bnl.zoomgov.com/meeting/register/vJItce2qqT8uGBmmwIMGhT2qQjv__TqOkB0)

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)

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09:00 → 09:45 Keeping up with the data: accelerating discovery in a high data velocity world

Speaker: Simon Billinge (Columbia University)

09:45 → 10:15 Towards a Deep Neural Network for General Structure-Property Mapping of Complex Systems

Speaker: Tom Penfold (Newcastle University)

10:15 → 10:45 First principles near-edge X-ray absorption spectra database development: Workflow, validation and benchmarking

Speaker: John Vinson (The National Institute of Standards and Technology)

10:45 → 10:55 Break

10:55 → 11:25 Classifying and predicting x-ray absorption spectra using machine learning

Speaker: Matthew Carbone (Columbia University)

11:25 → 11:55 Latent Space Analysis of Spectra

Speaker: Anatoly Frenkel (Stony Brook University)

11:55 → 13:00 Break

13:00 → 13:45 Integrating Theory with Machine Learning in Accelerating Materials Characterization

Speaker: Maria Chan (Argonne National Laboratory)

13:45 → 14:15 TBD

Speaker: Alex Hexemer (Lawrence Berkeley National Laboratory)

14:15 → 14:45 Bypassing Quantum Mechanics for Material Science with Machine Learning

Speaker: Emine Kucukbenli (Boston University)

14:45 → 14:55 Break

14:55 → 15:25 Time-resolved RIXS theory and applications in quantum materials

Speaker: Yao Wang (Clemson University)

15:25 → 15:55 Analysis of Synchrotron Extended X-ray Absorption Fine Structure (EXAFS) Data Using Artificial Intelligence Techniques

Speaker: Jeff Terry (Illinois Institute of Technology)

15:55 → 16:25 Artificial neural networks inverting 2p XAS spectra

Speaker: Johann Luder (National Sun Yat-sen University)