Machine learning dielectric screening for the simulation of excited state properties of molecules and materials.

ABSTRACT: Accurate and efficient calculations of absorption spectra of molecules and materials are essential for the understanding and rational design of broad classes of systems. Solving the Bethe-Salpeter equation (BSE) for electron-hole pairs usually yields accurate predictions of absorption spectra, but it is computationally expensive, especially if thermal averages of spectra computed for multiple configurations are required. We present a method based on machine learning to evaluate a key quantity entering the definition of absorption spectra: the dielectric screening. We show that our approach yields a model for the screening that is transferable between multiple configurations sampled during first principles molecular dynamics simulations; hence it leads to a substantial improvement in the ef

SUBMITTER: Dong SS 

PROVIDER: S-EPMC8179553 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature