Understanding finite-temperature dynamics of the spin-orbital-entangled magnet FeI2

ORAL

Abstract

The dynamical correlation functions of interacting quantum spin systems can be directly probed by spectroscopic experiments such as inelastic neutron or x-ray scattering. Semi-classical approximations based on spin coherent state offer an efficient and universal approach to compute these correlation functions and produce useful insights for a generic system with low entanglement. To recover quantum-mechanical results, a well-known renormalization scheme based on the quantum-classical correspondence principle for the harmonic normal modes is commonly used in the low-temperature regime. It is not clear how to extend this renormalization scheme to arbitrary high temperatures. In this talk, I will introduce a temperature-dependent normalization of the classical moments, whose magnitude is determined by imposing the quantum sum rule. Using temperature-dependent spin dynamics of a spin-orbital-entangled magnet Fe2 as a benchmark case, I will show that this simple renormalization scheme leads to a substantial improvement in the agreement between the calculated and measured dynamical spin structure factors at all temperatures. Our study establishes a necessary and minimal approach to obtain best agreement with experimental data of a quantum spin system using semi-classical simulations.

* The work of X.B. at LSU was supported by the Louisiana Board of Regents Support Fund. The work of K.B. was supported by the LANL LDRD program. The work of D.D. and C.D.B. at UTK, and D.B., M.M., and X.B. (earlier work) at GT was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences, and Engineering Division under award DE-SC-0018660.

Presenters

  • Xiaojian Bai

    Louisiana State University

Authors

  • Xiaojian Bai

    Louisiana State University

  • David A Dahlbom

    University of Tennessee, Knoxville, Oak Ridge National Laboratory

  • Faith T Brooks

    Georgia Institute of Technology

  • Matthew S Wilson

    Los Alamos National Laboratory

  • Songxue Chi

    Oak Ridge National Laboratory

  • Alexander I Kolesnikov

    Oak Ridge National Lab, Oak Ridge National Laboratory

  • Matthew B Stone

    Oak Ridge National Laboratory

  • Huibo Cao

    Oak Ridge National Laboratory

  • Ying Wai Li

    Los Alamos National Laboratory, los alamos national laboratory

  • Kipton Barros

    Los Alamos Natl Lab, Los Alamos National Laboratory

  • Martin P Mourigal

    Georgia Tech, Georgia Institute of Technology

  • Cristian D Batista

    University of Tennessee