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.
Chitturi, Sathya R., Ji, Zhurun, Petsch, Alexander N., et al., "Capturing dynamical correlations using implicit neural representations," Nature Communications 14, no. 1 (2023), https://doi.org/10.1038/s41467-023-41378-4
@article{osti_2001152,
author = {Chitturi, Sathya R. and Ji, Zhurun and Petsch, Alexander N. and Peng, Cheng and Chen, Zhantao and Plumley, Rajan and Dunne, Mike and Mardanya, Sougata and Chowdhury, Sugata and Chen, Hongwei and others},
title = {Capturing dynamical correlations using implicit neural representations},
annote = {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 La 2 NiO 4 , showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems. },
doi = {10.1038/s41467-023-41378-4},
url = {https://www.osti.gov/biblio/2001152},
journal = {Nature Communications},
issn = {ISSN 2041-1723},
number = {1},
volume = {14},
place = {United Kingdom},
publisher = {Nature Publishing Group},
year = {2023},
month = {09}}
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States). Linac Coherent Light Source (LCLS)
Sponsoring Organization:
Engineering and Physical Sciences Research Council (EPSRC); USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE)
Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 834https://doi.org/10.1016/j.nima.2016.07.036
Weinfurther, Kyle; Mattingly, John; Brubaker, Erik
Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 883https://doi.org/10.1016/j.nima.2017.11.025