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Accurate prediction of infrared dielectric functions in polar materials isfundamental for thermal and photonic applications, yet it remains unexploredwhether the two main methods, Green-Kubo formula and Lorentz model, can giveunified predictions. In this work, we present a detailed comparison of thesetwo approaches using MgO and LiH as prototypical cases employing both empiricalrigid ion model (RIM) and machine learning potential (MLP). We demonstrate thatthe conventional Lorentz model fails to capture the multi-phonon absorptioninherent in Green-Kubo method, which can be resolved via using the phononself-energy as a generalization of the usual linewidth. In addition, with RIM,a correction factor is required in the ionic contribution to infrared responseto account for the electronic polarization effect, which is yet captured by MLPusing the Born effective charges for calculating dipole moment. The presentbenchmark study thus enables cross-validation of dielectric functioncalculations while providing mechanistic insights into the polarizationdynamics.

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