Virtual Talk: HPC+AI-Enabled Real-Time Coherent X-ray Diffraction Imaging

Mathew Cherukara
Argonne National Laboratory

HPC+AI-Enabled Real-Time Coherent X-ray Diffraction Imaging

Mathew J. Cherukara, Advanced Photon Source, Argonne National Laboratory

Abstract: The capabilities provided by next generation light sources such as the Advanced Photon Source Upgrade (APSU) along with the development of new characterization techniques and detector advances are expected to revolutionize materials characterization (metrology) by providing the ability to perform scale-bridging, multi-modal materials characterization under in-situ and operando conditions. For example, providing the ability to image in 3D large fields of view (~mm3) at high resolution (<10 nm), while simultaneously acquiring information about structure, strain, elemental composition, oxidation state, photovoltaic response etc.

However, these novel capabilities dramatically increase the complexity and volume of data generated by instruments at the new light sources. Conventional data processing and analysis methodologies become infeasible in the face of such large and varied data streams. The use of AI/ML methods is becoming indispensable for real-time analysis, data abstraction and decision making at advanced synchrotron light sources such as the APS. I will describe the use high-performance computing (HPC) along with AI on edge devices to enable real-time analysis of streaming data from x-ray imaging instruments at the APS.

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2. Yao, Y., Chan, H., Sankaranarayanan, S., Balaprakash, P., Harder, R. J., & Cherukara, M. J. (2022). AutoPhaseNN: unsupervised physics-aware deep learning of 3D nanoscale Bragg coherent diffraction imaging. npj Computational Materials, 8(1), 1-8.
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7. Cherukara, M. J., Nashed, Y. S., & Harder, R. J. (2018). Real-time coherent diffraction inversion using deep generative networks. Scientific reports, 8(1), 1-8.

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