The 'performance trajectory' shown in Figure is generated by gradually increasing the amount of training data available to the model. The model is trained in the same manner as described previously, actually containing four separate networks trained on different random selections of the downsampled data. At first the model sees only a small number of experiments during training before subsequent testing on the holdout sets. The amount is gradually increased to the full size of the dataset, analogous to performing more solubility experiments to gather more samples in hopes of improving model performance.