Progress towards a rapid-throughput MeV ultrafast electron diffraction system

ORAL

Abstract

MeV ultrafast electron diffraction (MUED) is a powerful structural measurement technique for novel characterization of matter. It can determine fine structural details with ultrafast time resolving capability, enabling the study of structural transitions in a wide range of materials.
One class of advancement that can be investigated at MUED facilities is the demonstration of realtime or near-realtime data processing enabled by data science/machine learning/artificial intelligence mechanisms in conjunction with high-performance computing for automated operation, data acquisition and processing. We present the current progress made towards fully automating the facility from operation to material characterization.

*Work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division, Program of Electron and Scanning Probe Microscopies, award DE-SC0021365 through DOE's Established Program to Stimulate Competitive Research State-National Laboratory Partnerships program in the Office of Basic Energy and Sciences. This research used resources of the Brookhaven National Laboratory's Accelerator Test Facility and of the Argonne Leadership Computing Facility, which are DOE Office of Science User Facilities.

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Presenters

  • Mariana Fazio

    • Electrical & Computer Engineering, University of New Mexico

Authors

  • Mariana Fazio

    • Electrical & Computer Engineering, University of New Mexico

  • Sandra G Biedron

    • Electrical and Computer Engineering and Mechanical Engineering, University of New Mexico

  • Destry Monk

    • Electrical & Computer Engineering, University of New Mexico

  • Manel Martínez-Ramón

    • Electrical & Computer Engineering, University of New Mexico

  • Salvador Sosa Guitron

    • Electrical & Computer Engineering, University of New Mexico

  • David Martin

    • Argonne National Laboratory

  • Michael Papka

    • Argonne National Laboratory

  • Marcus Babzien

    • Brookhaven National Laboratory

  • Kevin A. Brown

    • Brookhaven National Laboratory

  • Mark A Palmer

    • Brookhaven National Laboratory

  • Jing Tao

    • Brookhaven National Laboratory

  • Alan Hurd

    • Los Alamos National Laboratory
    • Los Alamos Natl Lab

  • Julian Chen

    • Los Alamos National Laboratory

  • Rohit P Prasankumar

    • Los Alamos National Laboratory
    • CINT, Los Alamos National Lab
    • Center for Integrated Nanotechnologies, Los Alamos National Laboratory

  • Christine Sweeney

    • Los Alamos National Laboratory