We currently use SciKit-Learn's out-of-the-box version of Gaussian Process which assumes one expected value for each feature vector, limiting the types of molecules we can use for training. Specifically, polyprotic molecules are not suitable for training input in this approach. Specifically, for polyprotic molecules, there is a feature vector for each microscopic transition, leading to more predicted values than experimental pK a values. For example, there are four microscopic transitions for a diprotic molecule but only two macroscopic pK a values. Thus, we focused our training set on instances where a microscopic transition can be directly mapped to an experimental macroscopic pK a . The first set of molecules was perhaps the most obvious -those with only one ionizable group where the microscopic and macroscopic transition are identical. We checked that molecules we identified as having a single ionizable group also only had one experimental measurement. This resulted in 2,672 molecules. To expand the diversity of the training set we added a selection of diprotic molecules. For this set we also included molecules where we identified two ionizable groups and two experimental values were reported. Additionally, we required the difference in these two experimental pK a s be greater than three log units to assure dominance of a single microstate in estimation of the macroscopic pK a . There were a total of 286 diprotic molecules in the database that met this requirement. For these molecules, we assumed each macroscopic pK a was dominated by only one microscopic transition.