Automated Workflow for Accurate High-Throughput GW Calculations

TL;DR

This paper introduces an automated, open-source workflow for G0W0 calculations that streamlines high-throughput excited-state property computations by reducing convergence complexity, validated against experimental data and applied to over 320 structures.

Contribution

The authors develop a fully-automated GW calculation workflow that estimates errors to minimize parameter searches, enabling efficient high-throughput excited-state property calculations.

Findings

Workflow reduces convergence time and complexity.

Constructed a database of QP energies for 320+ structures.

Validated accuracy against experimental and state-of-the-art data.

Abstract

The GW approximation represents the state-of-the-art ab-initio method for computing excited-state properties. Its execution requires control over a larger number of (often interdependent) parameters, and therefore its application in high-throughput studies is hindered by the intricate and time-consuming convergence process across a multi-dimensional parameter space. To address these challenges, here we develop a fully-automated open-source workflow for G$_0$W$_0$ calculations within the AiiDA-VASP plugin architecture. The workflow is based on an efficient estimation of the errors on the quasi-particle (QP) energies due to basis-set truncation and the pseudo-potential norm violation, which allows a reduction of the dimensionality of the parameter space and avoids the need for multi-dimensional convergence searches. Protocol validation is conducted through a systematic comparison against established experimental and state-of-the-art GW data. To demonstrate the effectiveness of the approach, we construct a database of QP energies for a diverse dataset of over 320 bulk structures. The openly accessible workflow and resulting dataset can serve as a valuable resource and reference for conducting accurate data-driven research.