Automation methodologies and large-scale validation for $GW$, towards high-throughput $GW$ calculations

摘要

The search for new materials, based on computational screening, relies onmethods that accurately predict, in an automatic manner, total energy,atomic-scale geometries, and other fundamental characteristics of materials.Many technologically important material properties directly stem from theelectronic structure of a material, but the usual workhorse for total energies,namely density-functional theory, is plagued by fundamental shortcomings anderrors from approximate exchange-correlation functionals in its prediction ofthe electronic structure. At variance, the $GW$ method is currently thestate-of-the-art {\em ab initio} approach for accurate electronic structure. Itis mostly used to perturbatively correct density-functional theory results, butis however computationally demanding and also requires expert knowledge to giveaccurate results. Accordingly, it is not presently used in high-throughputscreening: fully automatized algorithms for setting up the calculations anddetermining convergence are lacking. In this work we develop such a method and,as a first application, use it to validate the accuracy of $G_0W_0$ using thePBE starting point, and the Godby-Needs plasmon pole model($G_0W_0^\textrm{GN}$@PBE), on a set of about 80 solids. The results of theautomatic convergence study utilized provides valuable insights. Indeed, wefind correlations between computational parameters that can be used to furtherimprove the automatization of $GW$ calculations. Moreover, we find that$G_0W_0^\textrm{GN}$@PBE shows a correlation between the PBE and the$G_0W_0^\textrm{GN}$@PBE gaps that is much stronger than that between $GW$ andexperimental gaps. However, the $G_0W_0^\textrm{GN}$@PBE gaps still describethe experimental gaps more accurately than a linear model based on the PBEgaps.