We seek applicability metrics for which molecules with larger applicability metrics have a greater likelihood of having larger errors than those with smaller metrics. The statistical variation in error means that we assume that these changes will be visible in averages and not that all points with larger metrics will have larger errors. Accordingly, we grouped our data into 16 bins based on each of the applicability metric and show the average errors and likelihood of an error being greater than 1 kcal/mol. In all cases, we used the predicted solvation energy in ACN based on the atomic contribution MPNNs for molecules in our Holdout and Pedley test sets. The results are shown in Figure .