als with a band gap because the largest subspace eigenvalue for metals as q → 0 can be orders of magnitude larger for metals than the one for materials with less screening. As an alternative, we instead first sorted all |λ SS,i |/ max(|λ SS |) largest to smallest, and then retained the first N λ of them and their associated v SS,i , where N λ is a user-specified integer less than or equal to the total possible basis set size of χ 0 . Note that the χ 0 basis set size used here was defined using the screened cutoff, which is less than the planewave cutoff used in the DFT calculation. N λ can also be specified via a fraction, f SS , of the total basis set size, which is the approach used here. We thus term this the eigenvalue fraction (EF) method. The errors in RPA correlation energy that result from using this approach are shown as the solid lines in Figure . For the quasi-0D systems f SS ∈ {1, 3, 5, 10, 20}%. For the quasi-2D systems f SS ∈ {20, 30, 40, 50, 60}%. For the 3D periodic systems f SS ∈ {20, 40, 60, 80, 90, 100}%. It is again apparent that system dimensionality is important in specifying f SS for the EF method. However, although either method could be used in practice, the RPA correlation energy for metals converges somewhat faster using the EF method as compared to the ET method. Both methods perform similarly for the other materials. This result also implies that better methods for representing χ 0 should be investigated to further reduce the cost of these calculations. Overall, we find that the EF method yields RPA correlation energy convergence of 1 meV/atom for the molecular, surface, and bulk systems with f SS of 3, 40, and 80%, respectively, with the exception of bulk Pt, which is converged to 5 meV/atom and would require f SS = 90% instead. Using f SS = 30% could potentially be used for surfaces as well because the errors of the MgO(001), MoS 2 , and Pt(111) surfaces were only 1, 3, and 14 meV/atom, respectively, which is still relatively small on the scale of catalytic activity.