for n = 6 or 8. In the context of XSAPT, we refer to Eq. ( ) as an "ab initio" dispersion potential (ai D), because the atomic parameters {C 6,a } and {C 8,a } were obtained by fitting to ab initio dispersion energies, primarily from SAPT2+(3) calculations. Notably, Eq. ( ) has the same form as the dispersion corrections used in DFT + D. Several other groups have explored the idea of replacing E (2) disp in SAPT0 with an empirical model, which has sometimes been called "SAPT0 + D" 127,128 but we prefer to reserve the SAPT0 to mean second-order dispersion and will instead call these models "SAPT(KS) + D"; see the discussion that accompanies Eq. ( ). In terms of the physics, Grimme's DFT + D3 and DFT + D4 dispersion corrections have been adapted for use with SAPT(HF) by refitting the damping parameters, leaving the C 6 and C 8 parameters unmodified. Unlike earlier dispersion corrections by beginning with D3 the atomic dispersion coefficients are computed from DFT polarizabilities and should resemble genuine dispersion when the monomers are well separated. 129 However, these corrections do not resemble genuine dispersion at vdW contact distances, because the damping is significant at that range. As a result, the energy components in these methods are not reliable even while the total interaction energies may be accurate. The same is true of our first-generation ai D1 model, whose parameters were fit to obtain total interaction energies in agreement with CCSD(T)/CBS benchmarks. It was subsequently discovered that SAPT(KS) + ai D1 benefits significantly from error cancellation, such that the energy components are not in good agreement with SAPT2+(3) benchmarks, and the same is true for SAPT(HF) + D3 and SAPT(HF) + D4 models. These methods yield good interaction energies at the expense of significant error cancellation between the dispersion and exchange-repulsion terms. 128 Energy decomposition is one of the primary motivations to use the SAPT framework, so we believe it is crucial to achieve high accuracy not only for E int but also for its components. For this reason, we do not recommend the first-generation ai D1 model