The materials and catalysts needed for renewable energy applications such as fuel cells, batteries, electrolyzers, and solar cells are complex and diverse, and understanding their properties requires accurate and performant quantum chemical modeling software. Although current density functional theory (DFT) packages are mature and widely used in chemistry research 1? -3 , packages for more expensive, but generally more accurate beyond-DFT approaches, such as the GW approximation and random phase approximation (RPA), are less well developed. Such packages typically are targeted towards certain material classes, especially semiconductors and molecules since these tend to show larger beyond-DFT exchange-correlation effects due to reduced electronic screening . Additionally, many implementations of beyond-DFT methods do not scale to large system sizes well due to parallelization limitations and the well-known quartic dependence of the computational resources needed on the system size (at minimum).