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This work is supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award No. DE-SC0022216, as well as under Contract DE-AC0276SF00515 for the Materials Sciences and Engineering Division and the Linac Coherent Light Source (LCLS). A portion of this research used resources at the Spallation Neutron Source, a DOE Office of Science User Facility operated by the Oak Ridge National Laboratory. J. J. Turner acknowledges support from the U.S. DOE, Office of Science, Basic Energy Sciences through the Early Career Research Program. Z. Ji is supported by the Stanford Science fellowship, and the Urbenek-Chodorow postdoctoral fellowship awards. A.N. Petsch acknowledges funding and support from the Engineering and Physical Sciences Research Council (EPSRC) under Grant Nos. EP/L015544/1 and EP/R011141/1.
Dataset of Linear Spin Wave Theory simulations accompanying the manuscript: Capturing dynamical correlations using implicit neural representations.
Machine Learning, Linear Spin Wave Theory, Neutron Scattering
Machine Learning, Linear Spin Wave Theory, Neutron Scattering
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