Reinforcement learning and neutron scattering

ORAL

Abstract

During this talk, I will discuss our recent progress with applying reinforcement learning to neutron scattering. Examples include single crystal diffraction, measurements of the order parameter, and measurement of spin-wave excitations. Our results are currently on simulated data and show that it is possible to use reinforcement learning to dramatically reduce the number of measurements required to obtain parameters from experiments. I will also discuss the advantages of incorporating physics into models

*We acknowledge funding from the United States Department of Commerce. We also acknowledge, The Center for High Resolution Neutron Scattering (CHRNS) a national user facility jointly funded by the NCNR and the NSF under Agreement No. DMR-2010792d.

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Presenters

  • William Ratcliff

    • GDS
    • National Institute of Standards and Tech
    • NIST

Authors

  • William Ratcliff

    • GDS
    • National Institute of Standards and Tech
    • NIST

  • Kate Meuse

    • Cornell University

  • Jessica Opsahl-Ong

    • Rice University

  • Paul Kienzle

    • National Institute of Standards and Technology, NIST Center for Neutron Research, Gaithersburg, MD
    • NIST
    • National Institute of Standards and Technology