Consistency between the Green-Kubo formula and Lorentz model for predicting the infrared dielectric function of polar materials

Abstract

Accurate prediction of infrared dielectric functions in polar materials is fundamental for thermal and photonic applications, yet it remains unexplored whether the two main methods, Green-Kubo formula and Lorentz model, can give unified predictions. In this work, we present a detailed comparison of these two approaches using MgO and LiH as prototypical cases employing both empirical rigid ion model (RIM) and machine learning potential (MLP). We demonstrate that the conventional Lorentz model fails to capture the multi-phonon absorption inherent in Green-Kubo method, which can be resolved via using the phonon self-energy as a generalization of the usual linewidth. In addition, with RIM, a correction factor is required in the ionic contribution to infrared response to account for the electronic polarization effect, which is yet captured by MLP using the Born effective charges for calculating dipole moment. The present benchmark study thus enables cross-validation of dielectric function calculations while providing mechanistic insights into the polarization dynamics.

14 pages, 12 figures

References in corpus (22)

• Quantum ESPRESSO toward the exascale
• Assessing the Performance of Recent Density Functionals for Bulk Solids
• Nuclear quantum effects enter the mainstream
• GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
• How to remove the spurious resonances from ring polymer molecular dynamics
• A deep potential model with long-range electrostatic interactions
• Polarization effects in ionic solids and melts
• Spectral analysis of non-equilibrium molecular dynamics: spectral phonon temperature and phonon local non-equilibrium in thin films and across interfaces
• Computational prediction of lattice thermal conductivity -- a comparison of molecular dynamics and Boltzmann transport approaches
• Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamics
• A many-body interatomic potential for ionic systems: application to MgO
• Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: Case studies on thermal properties of materials
• Insight into the effect of force error on the thermal conductivity from machine-learned potentials
• Lattice dielectric properties of rutile ${\mathrm{TiO}}_2$: First-principles anharmonic self-consistent phonon study
• Infrared Optical Properties of Ferropericlase (Mg1-xFexO): Experiment and Theory
• The dielectric response of rock-salt crystals at finite temperatures from first principles
• Lattice thermal conductivity of ZrSe2 based on the anharmonic phonon approach and on-the-fly machine learning force fields
• Unconventional Thermophotonic Charge Density Wave
• Understanding the flat thermal conductivity of La2Zr2O7 at ultrahigh temperatures
• Temperature-Dependent Full Spectrum Optical Responses of Semiconductors from First Principles
• Atomistic modeling of extreme near-field heat transport across nanogaps between two polar dielectric materials
• A replacement of the Lorentz law for the shape of the spectral lines in the infrared region

Cited by in corpus (1)

• Crystal Orientation Dependence of Extreme Near-Field Heat Transfer between Polar Materials Governed by Surface Phonon Modes