Capturing dynamical correlations using implicit neural representations.

Autor: Chitturi SR; SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA. chitturi@stanford.edu.; Department of Materials Science and Engineering, Stanford University, Stanford, CA, 94305, USA. chitturi@stanford.edu., Ji Z; Department of Physics and Applied Physics, Stanford University, Stanford, CA, 94305, USA. zhurun@stanford.edu.; Geballe Laboratory for Advanced Materials, Stanford University, Stanford, CA, 94305, USA. zhurun@stanford.edu., Petsch AN; SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA. apetsch@stanford.edu.; Stanford Institute for Materials and Energy Sciences, Stanford University, Stanford, CA, 94305, USA. apetsch@stanford.edu.; H.H. Wills Physics Laboratory, University of Bristol, Bristol, BS8 1TL, UK. apetsch@stanford.edu., Peng C; Stanford Institute for Materials and Energy Sciences, Stanford University, Stanford, CA, 94305, USA., Chen Z; Stanford Institute for Materials and Energy Sciences, Stanford University, Stanford, CA, 94305, USA., Plumley R; SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA.; Stanford Institute for Materials and Energy Sciences, Stanford University, Stanford, CA, 94305, USA.; Department of Physics, Carnegie Mellon University, Pittsburgh, PA, 15213, USA., Dunne M; SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA., Mardanya S; Department of Physics and Astrophysics, Howard University, Washington, DC, USA., Chowdhury S; Department of Physics and Astrophysics, Howard University, Washington, DC, USA., Chen H; Department of Physics, Northeastern University, Boston, USA., Bansil A; Department of Physics, Northeastern University, Boston, USA., Feiguin A; Department of Physics, Northeastern University, Boston, USA., Kolesnikov AI; Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA., Prabhakaran D; Department of Physics, University of Oxford, Clarendon Laboratory, Oxford, OX1 3PU, UK., Hayden SM; H.H. Wills Physics Laboratory, University of Bristol, Bristol, BS8 1TL, UK., Ratner D; SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA., Jia C; SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA.; Stanford Institute for Materials and Energy Sciences, Stanford University, Stanford, CA, 94305, USA.; Department of Physics, University of Florida, Gainesville, FL, 32611, USA., Nashed Y; SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA., Turner JJ; SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA. joshuat@slac.stanford.edu.; Stanford Institute for Materials and Energy Sciences, Stanford University, Stanford, CA, 94305, USA. joshuat@slac.stanford.edu.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2023 Sep 20; Vol. 14 (1), pp. 5852. Date of Electronic Publication: 2023 Sep 20.
DOI: 10.1038/s41467-023-41378-4
Abstrakt: 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 La 2 NiO 4 , showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems.
(© 2023. Springer Nature Limited.)
Databáze: MEDLINE
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