Capturing dynamical correlations using implicit neural representations.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 37730824.
- Also identified by DOI 10.1038/s41467-023-41378-4 and PMC identifier 10511537.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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<sub>2</sub>NiO<sub>4</sub>, showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems.