a particular eigenvalue/vector. One simple approach is only retaining an eigenvalue, λ SS,i , and its associated eigenvector, v SS,i , if |λ SS,i |/ max(|λ SS |) ≥ δ SS , where δ SS is a small positive threshold defined by the user. We term this the eigenvalue threshold (ET) method. The eigenvalue normalization helps generalize the approach since different materials could have eigenvalues with drastically different magnitudes. This can clearly be seen in the subspace eigenvalue spectra shown in Figure , where max(|λ SS |) varies by two orders of magnitude. The errors in RPA correlation energy using an ET for a subset of the tested materials are shown as the dashed lines in Figure for δ SS ∈ {10 -2 , 10 -3 , 10 -4 , 10 -5 , 10 -6 }. Results for additional materials are shown in Figures S3. The x-axis is calculated using the q-point weighted average fraction of λ SS meeting the selection criterion, f SS . We find that the dimensionality of the material has a larger impact on RPA correlation energy convergence than material type in this test set, with the molecular systems converging much faster than the 3D periodic materials. This observation is consistent with the eigenvalues of these materials decaying to 0 in Figure as the fraction of the total basis set size retained increases. Using δ SS = 10 -4 yields RPA correlation energy convergence of at least 1 meV/atom, except for Pt(111), Pt(111)+O, MoS 2 , and (2×1×1) Pt, which are converged to within 5, 3, 2, and 3 meV/atom, respectively. This threshold could be further lowered if additional accuracy is desired. However, the ET method may not be expected to perform equally well for both metallic materials and materi-FIG. . Error in Ecorr,RP A per atom relative to the Ecorr,RP A per atom calculated using the most λSS for either the ET or EF methods as a function of fSS for different materials. The screened cutoff used was 20 Ryd. Solid (dashed) lines denote RPA correlation energies calculated with the EF (ET) method. From left to right, a quasi-0D molecule, quasi-2D surfaces, and 3D periodic materials need more subspace eigenvalues/vectors to retain the same computational accuracy.