Automation methodologies and large-scale validation for GW: Towards high-throughput GW calculations

• Published in: Physical review. B 2017-10, Vol.96 (15)
• Main Authors: van Setten, M J, Giantomassi, M, Gonze, X, Rignanese, G-M, Hautier, G
• Format: Article
• Language: English
• Subjects: Automation; Computation; Convergence; Density functional theory; Electronic structure; Material properties; Mathematical models; Predictions; Screening
• ISSN: 2469-9950; 2469-9969
• DOI: 10.1103/PhysRevB.96.155207

The search for new materials based on computational screening relies on methods 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 the electronic structure of a material, but the usual workhorse for total energies, namely density-functional theory, is plagued by fundamental shortcomings and errors from approximate exchange-correlation functionals in its prediction of the electronic structure. At variance, the GW method is currently the state-of-the-art ab initio approach for accurate electronic structure. It is mostly used to perturbatively correct density-functional theory results, but is, however, computationally demanding and also requires expert knowledge to give accurate results. Accordingly, it is not presently used in high-throughput screening: fully automatized algorithms for setting up the calculations and determining convergence are lacking. In this paper, we develop such a method and, as a first application, use it to validate the accuracy of G0W0 using the PBE starting point and the Godby-Needs plasmon-pole model (G0W0GN@PBE) on a set of about 80 solids. The results of the automatic convergence study utilized provide valuable insights. Indeed, we find correlations between computational parameters that can be used to further improve the automatization of GW calculations. Moreover, we find that G0W0GN@PBE shows a correlation between the PBE and the G0W0GN@PBE gaps that is much stronger than that between GW and experimental gaps. However, the G0W0GN@PBE gaps still describe the experimental gaps more accurately than a linear model based on the PBE gaps. With this paper, we hence show that GW can be made automatic and is more accurate than using an empirical correction of the PBE gap, but that, for accurate predictive results for a broad class of materials, an improved starting point or some type of self-consistency is necessary.