We score & optimize the output conformers with the AIMNet2 neural network potential and use the lowest energy conformer for subsequent calculations. Following Peter Kenny, we predict the basicity of each hydrogen-bond acceptor by computing V min around the hydrogen-bond-accepting atom (using the low-cost "Swiss-army knife" r 2 SCAN-3c method) and linearly scaling these potentials to match experimental pK BHX values. Electrostatic potential minima were located by numerical minimization of the electrostatic potential (computed via the ESPPropCalc object in Psi4) with the BFGS algorithm in Scipy. All density-functional-theory computations were conducted with Psi4 1.9.1. 33 (The default settings in Psi4 were modified somewhat: a (99,590) integration grid with "robust" pruning, the Stratmann-Scuseria-Frisch quadrature scheme was employed, and an integral tolerance of 10 -14 was used throughout. ) Density fitting was employed for all calculations and a level shift of 0.10 Hartree was applied to accelerate SCF convergence. 37 AIMNet2 geometry optimizations were run using geomeTRIC 1.0.2. To find values for the scaling constants, we employed Kenny's 2020 database of experimentally measured pK BHX values, and predicted the measured per-molecule pK BHX values by combining the predicted pK BHX values from each distinct electrostatic potential minimum (up to three per hydrogen-bond acceptor, following Equation 3 from Kenny et al. ). A total of 434 molecules were employed after excluding those for which 3D conformations could not be generated using ETKDG. We used Levenberg-Marquardt least-squares fitting as implemented in LMFIT to solve the resulting non-linear optimization problem.