Capturing dynamical correlations using implicit neural representations

作者：Sathya R. Chitturi, Zhurun Ji, Alexander N. Petsch, Peng Cheng, Zhantao Chen, Rajan Plumley, Mike Dunne, Sougata Mardanya, Sugata Chowdhury, Hongwei Chen, Arun Bansil, Adrian Feiguin, А. И. Колесников, D. Prabhakaran, S. M. Hayden, Daniel Ratner, Chunjing Jia, Youssef S. G. Nashed, Joshua J. Turner · 发表于：Nature Communications · 年份：2023 · DOI：10.1038/s41467-023-41378-4 · 被引用次数：11 · 研究领域：Neural Networks and Applications、Blind Source Separation Techniques、Model Reduction and Neural Networks

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.