AI-Enabled X-ray Science at the Advanced Photon Source

ORAL  · Invited

Abstract

The capabilities provided by next generation light sources such as the APSU along with the development of new characterization techniques and detector advances are expected to revolutionize materials characterization 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 characterization instruments at the APS.

*This work was performed at the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility, operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. This work was also supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences Data, Artificial Intelligence and Machine Learning at DOE Scientific User Facilities program under Award Number 34532. Support is also acknowledged from Argonne LDRD 2021-0090 – AutoPtycho: Autonomous, Sparse-sampled Ptychographic Imaging.

–

Publication: 1. Kim, J. W., Cherukara, M. J., Tripathi, A., Jiang, Z., & Wang, J. (2021). Inversion of coherent surface scattering images via deep learning network. Applied Physics Letters, 119(19), 191601.
2. Yao, Y., Chan, H., Sankaranarayanan, S., Balaprakash, P., Harder, R. J., & Cherukara, M. J. (2021). AutoPhaseNN: Unsupervised Physics-aware Deep Learning of 3D Nanoscale Coherent Imaging. arXiv preprint arXiv:2109.14053.
3. Zhou, T., Cherukara, M., & Phatak, C. (2021). Differential programming enabled functional imaging with Lorentz transmission electron microscopy. npj Computational Materials, 7(1), 1-8.
4. Chan, H., Nashed, Y. S., Kandel, S., Hruszkewycz, S. O., Sankaranarayanan, S. K., Harder, R. J., & Cherukara, M. J. (2021). Rapid 3D nanoscale coherent imaging via physics-aware deep learning. Applied Physics Reviews, 8(2), 021407.
5. Cherukara, M. J., Zhou, T., Nashed, Y., Enfedaque, P., Hexemer, A., Harder, R. J., & Holt, M. V. (2020). AI-enabled high-resolution scanning coherent diffraction imaging. Applied Physics Letters, 117(4), 044103.
6. 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.

Presenters

  • Mathew Cherukara

Authors

  • Mathew Cherukara