Knowing the pKa of a molecule is key to understanding its structure and reactivity. In medicinal chemistry, pKa values are used to predict a variety of important pharmacological properties, including solubility, hERG potassium channel blocking, phospholipidosis risk, CYP inhibition, and blood-brain-barrier penetration. Efficient and accurate pKa estimates play a pivotal role in the design of compounds with the desired physicochemical and pharmacological properties, and accordingly pKa prediction has been recognized as a problem of "immense interest" 7 in computational chemistry for decades. The most common approach to pKa prediction is to develop an empirical quantitative structure-property relationship (QSPR) based on experimentally measured data and use this model to predict pKa values for new compounds. QSPR approaches are typically fast and display excellent accuracy for compounds like those in the training data, making them an appealing choice for high-throughput pKa prediction. However, these methods struggle to extrapolate to unseen compounds and are generally unable to capture factors that arise from specific configurations of molecules in three-dimensional space, like conformational or stereoelectronic effects. A contrasting approach is to directly predict pKa values using electronic structure theory, since microscopic pKa values are directly proportional to the ∆G of the underlying acid dissociation reactions. This strategy possesses certain advantages over QSPR approaches: conformational and stereochemical effects are naturally incorporated, and new regions of chemical space can be modeled without new training data. In recent pKa prediction challenges, approaches based on electronic structure theory have consistently been among the best-performing methods: in SAMPL6, submission xvxzd achieved a mean absolute error (MAE) of 0.58 pKa units and a root-mean-squared error (RMSE) of 0.68, while submission EC_RISM achieved an MAE of 0.53 and an RMSE of 0.73 in SAMPL7. Unfortunately, many electronic structure theory-based approaches are too computationally costly for routine usage: in their paper describing the xvxzd submission to SAMPL6, Pracht and Grimme write that "a macroscopic pKa value for a molecule could be calculated within approximately one day on a 28 CPU node," which corresponds to roughly $25-30 with current cloud computing prices. While semiempirical methods like PM6 17 and GFN2-xtb have been used for rapid pKa prediction, the resulting workflows display lower accuracy and often require judicious choice of reference compound or significant reparameterization. This is unsurprising: calculation of acid dissociation constants is "demanding and arduous," and both a sophisticated electronic structure method and a sizable basis set are required to consistently get accurate results even in the gas phase. Machine-learned interatomic potentials have recently emerged as an efficient alternative to conventional electronic structure theory. We envisioned that AIMNet2, which displays accuracy comparable to routine density-functional theory methods for main-group thermochemistry, might allow for rapid computation of pKa values while preserving the advantages of electronic structure-based approaches. Here, we report the successful implementation of this strategy into a workflow ("Rowan pKa"), which uses AIMNet2 to calculate microscopic pKa values with good accuracy and minimal empiricism.