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The Center for Advanced Mathematics for Energy Research Applications (CAMERA): Advancing the Capabilities of National Scientific User Facilities Through Innovative Mathematics

ORAL

Abstract

Technological advancements at national scientific user facilities are creating opportunities to make unprecedented measurements of complex physical systems, potentially leading to groundbreaking discoveries across a spectrum of scientific domains. However, new experiments enabled by these technologies are producing data so large and complex that efficiently analyzing it is increasingly becoming a major bottleneck. New mathematics is critically needed to overcome this bottleneck and fully realize the potential of these experiments.

To tackle these mathematical challenges, the Center for Advanced Mathematics for Energy Research Applications (CAMERA) was formed through joint funding by the Office of Advanced Scientific Computing Research (ASCR) and the Office of Basic Energy Sciences (BES) within the US Department of Energy's Office of Science. CAMERA’s mission is to identify areas in experimental science that can be aided by new mathematical insights, develop the needed algorithmic tools, and deliver them as user-friendly software to the experimental community.

This presentation will provide an overview of new experimental capabilities enabled by advanced mathematical techniques developed at CAMERA. Examples include physics-informed Gaussian-Process regression and optimization for autonomous experimentation, multi-tiered iterative projection algorithms for solving complex 3D reconstruction problems, fully automated image registration to correct data misalignments and jitter, nonuniform FFT and GPU acceleration for rapid model-based iterative tomographic reconstruction, advanced machine learning architectures to allow image analysis from limited and noisy data, and more.

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Publication: Donatelli, Jeffrey J., Peter H. Zwart, and James A. Sethian. "Iterative phasing for fluctuation X-ray scattering." Proceedings of the National Academy of Sciences 112.33 (2015): 10286-10291.<br><br>Kurta, Ruslan P., et al. "Correlations in scattered X-ray laser pulses reveal nanoscale structural features of viruses." Physical review letters 119.15 (2017): 158102.<br><br>Donatelli, Jeffrey J., James A. Sethian, and Peter H. Zwart. "Reconstruction from limited single-particle diffraction data via simultaneous determination of state, orientation, intensity, and phase." Proceedings of the National Academy of Sciences 114.28 (2017): 7222-7227.<br><br>Pande, Kanupriya, et al. "Ab initio structure determination from experimental fluctuation X-ray scattering data." Proceedings of the National Academy of Sciences 115.46 (2018): 11772-11777.<br><br>Pelt, Daniël M., Kees Joost Batenburg, and James A. Sethian. "Improving tomographic reconstruction from limited data using mixed-scale dense convolutional neural networks." Journal of Imaging 4.11 (2018): 128.<br><br>Pelt, Daniël M., and James A. Sethian. "A mixed-scale dense convolutional neural network for image analysis." Proceedings of the National Academy of Sciences 115.2 (2018): 254-259.<br><br>Araujo, Flavio HD, et al. "Reverse image search for scientific data within and beyond the visible spectrum." Expert Systems with Applications 109 (2018): 35-48.<br><br>Noack, Marcus M., et al. "A kriging-based approach to autonomous experimentation with applications to x-ray scattering." Scientific reports 9.1 (2019): 11809.<br><br>Donatelli, Jeffrey J., and John CH Spence. "Inversion of many-beam Bragg intensities for phasing by iterated projections: Removal of multiple scattering artifacts from diffraction data." Physical review letters 125.6 (2020): 065502.<br><br>Noack, Marcus M., et al. "Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels." Scientific reports 10.1 (2020): 17663.<br><br>Noack, Marcus M., et al. "Gaussian processes for autonomous data acquisition at large-scale synchrotron and neutron facilities." Nature Reviews Physics 3.10 (2021): 685-697.<br><br>Noack, Marcus M., and James A. Sethian. "Advanced stationary and nonstationary kernel designs for domain-aware gaussian processes." Communications in Applied Mathematics and Computational Science 17.1 (2022): 131-156.<br><br>Pande, K., et al. "Joint iterative reconstruction and 3D rigid alignment for X-ray tomography." Optics Express 30.6 (2022): 8898-8916.<br><br>Segev-Zarko, Li-av, et al. "Cryo-electron tomography with mixed-scale dense neural networks reveals key steps in deployment of Toxoplasma invasion machinery." PNAS nexus 1.4 (2022): pgac183.<br><br>Fioravante de Siqueira, Alexandre, Daniela M. Ushizima, and Stéfan J. van der Walt. "A reusable neural network pipeline for unidirectional fiber segmentation." Scientific data 9.1 (2022): 32.<br><br>Noack, Marcus M., et al. "Exact Gaussian processes for massive datasets via non-stationary sparsity-discovering kernels." Scientific reports 13.1 (2023): 3155.<br><br>Huang, Ying, et al. "Detecting lithium plating dynamics in a solid-state battery with operando X-ray computed tomography using machine learning." npj Computational Materials 9.1 (2023): 93.<br><br>Chagnon, Eric, et al. "Benchmarking topic models on scientific articles using BERTeley." Natural Language Processing Journal 6 (2024): 100044.<br><br>Shah, Niteya, et al. "Optimizing and scaling the 3D reconstruction of single-particle imaging." 2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2024.<br><br>Kumar, Dinesh, Dilworth Y. Parkinson, and Jeffrey J. Donatelli. "tomoCAM: fast model-based iterative reconstruction via GPU acceleration and non-uniform fast Fourier transforms." Journal of Synchrotron Radiation 31.1 (2024).<br><br>Sordo, Zineb, et al. "RhizoNet segments plant roots to assess biomass and growth for enabling self-driving labs." Scientific Reports 14.1 (2024): 12907.<br><br>Hu, Z. and Donatteli, J.J., "A Multi-Tiered Computational Methodology for Extracting 3D Rotational Diffusion Coefficients from X-ray Photon Correlation Spectroscopy Data without Structural Information" (In review)<br><br>Donatelli, J.J., Chu, M., Jiang, Z., Schwarz, N., and Sethian, J.A., "Nonuniform Iterative Phasing Framework for 3D Dynamical Inversion from Coherent Surface Scattering Imaging" (In Preparation)

Presenters

  • Jeffrey Donatelli

    Lawrence Berkeley National Laboratory

Authors

  • Jeffrey Donatelli

    Lawrence Berkeley National Laboratory

  • James A Sethian

    UC Berkeley and Lawrence Berkeley National Laboratory

  • Marcus Noack

    Lawrence Berkeley National Laboratory

  • Zineb Sordo

    Lawrence Berkeley National Laboratory

  • Daniela Ushizima

    University of California, Berkeley

  • Petrus H Zwart

    Lawrence Berkeley National Laboratory

  • Zixi Hu

    Lawrence Berkeley National Laboratory

  • Kanupriya Pande

    Lawrence Berkeley National Laboratory

  • Dinesh Kumar

    Lawrence Berkeley National Laboratory

  • Ronald J Pandolfi

    Lawrence Berkeley National Laboratory

  • Harinarayan Krishnan

    Lawrence Berkeley National Laboratory

  • Alexander Hexemer

    Lawrence Berkeley National Laboratory

  • Eric Chagnon

    Lawrence Berkeley National Laboratory

  • Daniel Pelt

    Leiden University

  • Dilworth Parkinson

    Lawrence Berkeley National Laboratory

  • David A Shapiro

    Lawrence Berkeley National Laboratory