Our final workflow gives a mean error of approximately 0.19 pK BHX units (Table ), comparable to previously reported methods. Most of the outliers are bulky amines (Figure ): electrostatic potential-based predictions seem to overestimate the basicity of these sites, since bulky groups will block the approach of hydrogen-bond donors like 4-fluorophenol while minimally perturbing the electrostatic potential of the lone pair. For instance, triisopropylamine has a predicted pK BHX value of 1.39 units, but an experimental pK BHX value of 0.30 units, likely due to the bulky substituents preventing access to the central nitrogen. This sequence of steps avoids time-consuming geometry optimizations at the DFT level of theory, instead using the AIMNet2 neural network potential to optimize structures and running a single low-cost r 2 SCAN-3c calculation on the final minimum. Predictions can be run for the entire dataset above in a few hours on a conventional laptop, and even 50atom systems run in fewer than 10 minutes. The above hydrogen-bond-acceptor-strengthprediction workflow has been implemented in the Rowan cloud computational-chemistry platform, making it simple to submit calculations and quickly visualize the relative strength and position of different hydrogen-bond acceptors (Figure ).