The effects of the data set size and additional features are investigated using the pre-training set prior to fine-tuning any models. The resulting test root-mean-square errors (RMSE) on the reaction and solvent splits are presented in Figure . The reaction and solvent splits each test the model's performance on unseen reactions in seen solvents and on seen reactions in unseen solvents. As previously explained, the pre-training set of around 7.8M data is divided into smaller subsets to identify the optimal data set size that can balance accuracy and training time. From Figure , it can be seen that the test error initially decreases with an increasing data set size and plateaus out from 500k for the reaction split. For the solvent split, the error continues to decrease at a higher data set size, but the change in the error is very small beyond 500k. Therefore, 500k is chosen as a final data set size for the pre-training set. This result was expected since only the number of reaction-solvent pairs increases with the increasing data set size whereas the number of unique reactions and solvents remains constant. The information gain from more reaction-solvent pairs is likely to saturate after the model sees enough data on each reaction and solvent, causing the errors to level out.