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Capturing dynamical correlations using implicit neural representations.

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This summary is machine-generated.

This study introduces a machine learning tool to analyze material excitation spectra. It precisely extracts magnetic exchange parameters from experimental data, advancing the study of ordered magnetic systems.

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Area of Science:

  • Condensed Matter Physics
  • Materials Science
  • Computational Physics

Background:

  • Collective excitations in materials are key to understanding many-body physics.
  • Dynamical structure factor (S(Q, ω)) is typically measured using inelastic neutron or X-ray scattering.
  • Analysis involves comparing experimental data with theoretical predictions.

Purpose of the Study:

  • To develop a data-driven analysis tool for spectrographic measurements.
  • To efficiently extract unknown parameters from experimental data using automatic differentiation.
  • To enable precise parameter extraction for ordered magnetic systems.

Main Methods:

  • Utilizing neural implicit representations tailored for spectrographic data.
  • Employing linear spin wave theory simulations for model training.
  • Applying automatic differentiation for parameter refinement.

Main Results:

  • Precise extraction of exchange parameters from inelastic neutron scattering data.
  • Successful application to the square-lattice spin-1 antiferromagnet La₂NiO₄.
  • Demonstration of a machine learning platform for advanced model refinement.

Conclusions:

  • The developed tool offers a viable pathway for automatic refinement of models in ordered magnetic systems.
  • Neural implicit representations show promise for analyzing complex material excitation spectra.
  • This data-driven approach enhances the understanding of magnetic phenomena in materials.