Before training, we removed 10% of these molecules to later serve as an internal test set, resulting in setting aside 243 monoprotic and 29 diprotic molecules, for a total of 301 data points. The training data then consisted of 2,186 monoprotic and 257 diprotic molecules. We then split the training data into thirds in order to use a three-fold cross validation method to evaluate the choice of a Gaussian process model and choose a kernel . To evaluate model performance, we considered RMSE, mean error, and correlation coefficients for each training and validation set pair. We judged model per-formance on training and cross-validation datasets in the context of learning curves for the purposes of model and feature selection. Statistical analysis from our final cross-validation sets and internal test sets can be found in our supporting information. All training data was recombined for our final Gaussian process model used to evaluate our internal test set and make predictions for SAMPL6.