Phys.org  December 23, 2024 Koopmans spectral functionals enable the prediction of spectral properties with state-of-the-art accuracy which relies on capturing the effects of electronic screening through scalar, orbital-dependent parameters. The parameters must be computed for every calculation, making Koopmans spectral functionals more expensive. Researchers in Switzerland developed a machine-learning model that can predict these screening parameters directly from orbital densities calculated at the density-functional theory (DFT) level. In two cases they showed that using the screening parameters predicted by this mode led to orbital energies that differ by less than 20 meV on average. This approach substantially reduced the run time […]