) for training data dimers are added explicitly, and dimer energy differences from all other (test set) dimers are replaced with the predicted energy differences from the machine learning model. The errors continue to decrease, albeit slowly, at higher training fractions, apart from a slight increase for oxalic acid between 30% and 40%. These dimer correction errors are very small, even at a 10% training fraction, considering the total energetic range of the dimer error corrections, which span the range -0.71 to +4.58 kJ/mol for oxalic acid and -4.06 to +8.50 kJ/mol for maleic hydrazide. The excellent performance of the Gaussian process predictions is partly due to the selection of training data by the max-min algorithm (Fig. ). Because the atomic symmetry function descriptors decay at longer distances, the greatest diversity in descriptor space is found at short intermolecular separations, where the differences between MP2 and FIT+DMA energies are greatest. The max-min algorithm iteratively selects for dimers distinct to those already in the training set, and so at low training fractions those dimers with significant energy differences will form the majority of training data. The dimer energy prediction errors translate directly to errors in the lattice energies (measured relative to the fully corrected FIT+DMA+MP2 landscape); for both molecules, we see that lattice energy errors are just over 1 kJ/mol at 20% training fractions (Fig. ). Thus, the magnitude of the errors introduced by machine learning the dimer energy corrections are comparable to typical energy differences between polymorphs and to the energy differences between low energy predicted structures on the oxalic acid landscape (see Fig. ). However, the MSE shows that the errors in dimer energy correction, and hence in the total lattice energies, are largely systematic; the systematic nature of the errors is also clear in scatter plots of predicted vs calculated energy corrections (see supporting information). Due to only a small number of dimers with long intermolecular separations being present in the training set at small training fractions (Fig. ), dimers in the test set with large intermolecular distances will all be predicted with similar energy differences. These contributions are small, but as predictions are added multiple times due to symmetry, the addition results largely in a systematic shift of the lattice energies. These errors should be unimportant when considering the energetic ranking of crystal structures, since systematic errors cancel when evaluating relative energies. We also note that, at higher training fractions, the selection of training data continues to be iteratively selected by intermolecular distance (see supporting information), indicating that the resolution for these highly separated dimers is still sufficient to distinguish them. Training fractions of 10% and 20% reduce the number of required MP2 calculations from 16,379 for the full FIT+DMA+MP2 model to 1,678 and 3,276, respectively. This 80 to 90% saving in time required for the MP2 correction will be increased if we use the GP model for lattice energy optimization.