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; Joshua J Turner

doi:10.1038/s41467-023-41378-4

Capturing dynamical correlations using implicit neural representations

Nat Commun. 2023 Sep 20;14(1):5852. doi: 10.1038/s41467-023-41378-4.

Authors

Sathya R Chitturi^{1

2}, Zhurun Ji^{3

4}, Alexander N Petsch^{5

6

7}, Cheng Peng⁸, Zhantao Chen⁸, Rajan Plumley^{9

8

10}, Mike Dunne⁹, Sougata Mardanya¹¹, Sugata Chowdhury¹¹, Hongwei Chen¹², Arun Bansil¹², Adrian Feiguin¹², Alexander I Kolesnikov¹³, Dharmalingam Prabhakaran¹⁴, Stephen M Hayden¹⁵, Daniel Ratner⁹, Chunjing Jia^{9

8

16}, Youssef Nashed⁹, Joshua J Turner^{17

18}

Affiliations

¹ SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA. [email protected].
² Department of Materials Science and Engineering, Stanford University, Stanford, CA, 94305, USA. [email protected].
³ Department of Physics and Applied Physics, Stanford University, Stanford, CA, 94305, USA. [email protected].
⁴ Geballe Laboratory for Advanced Materials, Stanford University, Stanford, CA, 94305, USA. [email protected].
⁵ SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA. [email protected].
⁶ Stanford Institute for Materials and Energy Sciences, Stanford University, Stanford, CA, 94305, USA. [email protected].
⁷ H.H. Wills Physics Laboratory, University of Bristol, Bristol, BS8 1TL, UK. [email protected].
⁸ Stanford Institute for Materials and Energy Sciences, Stanford University, Stanford, CA, 94305, USA.
⁹ SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA.
¹⁰ Department of Physics, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
¹¹ Department of Physics and Astrophysics, Howard University, Washington, DC, USA.
¹² Department of Physics, Northeastern University, Boston, USA.
¹³ Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
¹⁴ Department of Physics, University of Oxford, Clarendon Laboratory, Oxford, OX1 3PU, UK.
¹⁵ H.H. Wills Physics Laboratory, University of Bristol, Bristol, BS8 1TL, UK.
¹⁶ Department of Physics, University of Florida, Gainesville, FL, 32611, USA.
¹⁷ SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA. [email protected].
¹⁸ Stanford Institute for Materials and Energy Sciences, Stanford University, Stanford, CA, 94305, USA. [email protected].

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.

Abstract

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