To test this, the training data size versus testing set accuracy learning curves were generated by training individual models for each architecture on subsets of the training data and evaluating their performance (Fig. ). Between training sample sizes of 10k and 40k, the models show large error reductions (MAE reductions from 8 kcal/mol to 5.5 kcal/mol in the largest cases), but for sizes between 75k and 100k, the models show average improvements of less than 1 kcal/mol.