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Capturing dynamical correlations using implicit neural representations

Author

Listed:
  • Sathya R. Chitturi

    (SLAC National Accelerator Laboratory
    Stanford University)

  • Zhurun Ji

    (Stanford University
    Stanford University)

  • Alexander N. Petsch

    (SLAC National Accelerator Laboratory
    Stanford University
    University of Bristol)

  • Cheng Peng

    (Stanford University)

  • Zhantao Chen

    (Stanford University)

  • Rajan Plumley

    (SLAC National Accelerator Laboratory
    Stanford University
    Carnegie Mellon University)

  • Mike Dunne

    (SLAC National Accelerator Laboratory)

  • Sougata Mardanya

    (Howard University)

  • Sugata Chowdhury

    (Howard University)

  • Hongwei Chen

    (Northeastern University)

  • Arun Bansil

    (Northeastern University)

  • Adrian Feiguin

    (Northeastern University)

  • Alexander I. Kolesnikov

    (Oak Ridge National Laboratory)

  • Dharmalingam Prabhakaran

    (University of Oxford, Clarendon Laboratory)

  • Stephen M. Hayden

    (University of Bristol)

  • Daniel Ratner

    (SLAC National Accelerator Laboratory)

  • Chunjing Jia

    (SLAC National Accelerator Laboratory
    Stanford University
    University of Florida)

  • Youssef Nashed

    (SLAC National Accelerator Laboratory)

  • Joshua J. Turner

    (SLAC National Accelerator Laboratory
    Stanford University)

Abstract

Understanding the nature and origin of collective excitations in materials is of fundamental importance for unraveling the underlying physics of a many-body system. Excitation spectra are usually obtained by measuring the dynamical structure factor, S(Q, ω), using inelastic neutron or x-ray scattering techniques and are analyzed by comparing the experimental results against calculated predictions. We introduce a data-driven analysis tool which leverages ‘neural implicit representations’ that are specifically tailored for handling spectrographic measurements and are able to efficiently obtain unknown parameters from experimental data via automatic differentiation. In this work, we employ linear spin wave theory simulations to train a machine learning platform, enabling precise exchange parameter extraction from inelastic neutron scattering data on the square-lattice spin-1 antiferromagnet La2NiO4, showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems.

Suggested Citation

  • Sathya R. Chitturi & Zhurun Ji & Alexander N. Petsch & Cheng Peng & Zhantao Chen & Rajan Plumley & Mike Dunne & Sougata Mardanya & Sugata Chowdhury & Hongwei Chen & Arun Bansil & Adrian Feiguin & Alex, 2023. "Capturing dynamical correlations using implicit neural representations," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41378-4
    DOI: 10.1038/s41467-023-41378-4
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    References listed on IDEAS

    as
    1. Anjana M. Samarakoon & Kipton Barros & Ying Wai Li & Markus Eisenbach & Qiang Zhang & Feng Ye & V. Sharma & Z. L. Dun & Haidong Zhou & Santiago A. Grigera & Cristian D. Batista & D. Alan Tennant, 2020. "Machine-learning-assisted insight into spin ice Dy2Ti2O7," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
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