The two geometries are maximally oriented using the Kabsch algorithm, in order to maximize the overlap. Each computation of η requires three ground state calculations to obtain the orbitals of the isolated donor molecule, the orbitals of the isolated acceptor molecule, and the orbitals of the D-A complex. The calculation of Oij is performed in a locally-modified version of Q-Chem. To assess the correlation between η and the S1 DCT of D-A dimers, we screened 31 D-A complexes with donor and acceptor molecules shown in Figure and whose S1 states are dominated by a HOMO → LUMO transition. The donor and acceptor molecules chosen are augmented from a data set recently chosen in a screening of S0 DCT. The donor molecules exhibit a diversity of molecular structures, while the acceptor molecules are 7,7,8,8-tetracyanoquinodimethane (TCNQ) and its fluorinated derivatives, F x TCNQ. Geometries are optimized with Gaussian G16 at the CAM-B3LYP/6-31+G(d,p) level of theory with Grimme dispersion. We calculated the DCT using natural bond orbital (NBO) population analysis implemented in Gaussian G16 and transition density matrix (TDM) analysis in Theodore. We investigate the impact of geometries used to compute η by plotting η vs. S1 DCT in two ways. First, we optimize both the isolated monomers and their dimer complex and compute η vs. S1 DCT (Figure , left). Second, we optimize the dimer complex and take the geometries of the isolated monomers to be the same as in their dimer complex (Figure , right). In each case, the S1 DCT is computed using TDM analysis. Notably, we observe significant variations in the η values when the monomers were optimized, as evidenced by the outliers shown in the left plot of Figure . Additionally, the R 2 value from linear regression is 0.66 when comparing optimized monomers vs. optimized dimers, while the R 2 value is 0.96 when monomer geometries are unrelaxed from those found in the dimer complex. In both cases, our analysis reveals a positive linear correlation between the S1 DCT and η, indicating that S1 DCT is large when the HOMO (LUMO) orbital of the donor (acceptor) retains its character from the isolated molecule. That the unrelaxed monomer geometries provide superior performance presents certain practical advantages, including avoiding the computational cost associated with optimizing the monomer geometries. Moreover, computing η at a single geometry eliminates the necessity of evaluating the atomic overlap integrals at displaced geometries. This simplifies the calculation of η, as Sµν becomes the atomic basis self-overlap matrix, which is commonly printed in the output of electronic structure programs. 31 To assess the sensitivity of η to the dimer geometry, bridging the gap between D-A dimers and D-A co-crystals, we perform an analysis of the S1 DCT vs. η for geometries from experimental crystallographic data (where available) and compare to the results found in Figure . S1 DCT is again computed using TDM analysis. Figure shows the results obtained using the geometries from experiment in comparison to the dimer geometries, and indicates that the positive linear correlation still holds when the D-A geometries are taken from experimental crystal structures. In fact, when linear regressions are performed separately, the trend lines are almost indistinguishable. In Table , we report the value of η computed at each geometry (dimer complex vs. experiment) and the percent deviation between the two. The values of η obtained from these two geometries have very small percent deviations, from 0.1% to 6% with an average of 1.3%. This shows that η can bridge between different types of molecular structures (isolated dimer and experimental crystal), indicating reliability of the metric for different data sources. This is challenging for predictive metrics that rely on orbital energies. We next assess two methodologies for calculating the correlation of S1 DCT with η. Using unrelaxed molecular geometries taken from the dimer complexes, Figure plots η vs. S1 DCT using either TDM analysis (as in Figure ), or alternatively using NBO analysis. The linear regression analysis of the TDM-analyzed data (R 2 = 0.96) is significantly improved compared to that of the NBO-analyzed data (R 2 = 0.57). The poor fit in the latter case is due to the method's inability to treat delocalized electron transfer. Conversely, TDM provides a spatial mapping of the electron-hole pair associated with an electronic transition between two states and can successfully treat such delocalized electron transfer. While both methods are commonly used to calculate the charges in molecules, we recommend TDM analysis over NBO.