Capturing dynamical correlations using implicit neural representations

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, Alexander I. Kolesnikov, Dharmalingam Prabhakaran, Stephen M. Hayden, Daniel Ratner, Chunjing Jia, Youssef Nashed and Joshua J. Turner ()
Additional contact information
Sathya R. Chitturi: SLAC National Accelerator Laboratory
Zhurun Ji: Stanford University
Alexander N. Petsch: SLAC National Accelerator Laboratory
Cheng Peng: Stanford University
Zhantao Chen: Stanford University
Rajan Plumley: SLAC National Accelerator Laboratory
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
Youssef Nashed: SLAC National Accelerator Laboratory
Joshua J. Turner: SLAC National Accelerator Laboratory

Nature Communications, 2023, vol. 14, issue 1, 1-8

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-41378-4

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