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 enabling the application of Koopmans spectral functionals to classes of problems that would have been prohibitively expensive. According to the researchers, their work demonstrated that measuring violations of piecewise linearity could be done efficiently by combining frozen-orbital approximations and machine learning… read more.
Open Access TECHNICAL ARTICLE
Two possible workflows for performing Koopmans spectral functional calculations. Credit: npj Computational Materials volume 10, Article number: 299, 20 December 2024