Predicting electronic screening for fast Koopmans spectral functional calculations.

ABSTRACT: Koopmans spectral functionals are a powerful extension of Kohn-Sham density-functional theory (DFT) that enables the prediction of spectral properties with state-of-the-art accuracy. The success of these functionals relies on capturing the effects of electronic screening through scalar, orbital-dependent parameters. These parameters have to be computed for every calculation, making Koopmans spectral functionals more expensive than their DFT counterparts. In this work, we present a machine-learning model that-with minimal training-can predict these screening parameters directly from orbital densities calculated at the DFT level. We show in two prototypical use cases that using the screening parameters predicted by this model, instead of those calculated from linear response, leads to orbita

SUBMITTER: Schubert Y 

PROVIDER: S-EPMC11659161 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature