The synthetic group additivity dataset was constructed using publicly available data and molecules. Group additivity coefficients were fitted to the enthalpies calculated for the 134 thousand molecules of the QM9 dataset 49 using ridge regression. Group structures were defined by a central non-hydrogen atom and the atoms and bonds within a 1-bond radius. Only groups that were represented at least 100 times in QM9 and molecules made up entirely of those groups were included in the regression. The group additivity coefficients were rounded to the nearest thousandth kcal/mol. No non-nearestneighbour group contributions were included for symmetry, ring strain, or other inter-or intramolecular interactions. The fitted coefficients were then applied to a larger set of molecules, the GDB11 set of over 26 million unique molecules containing up to 11 C/N/O/F atoms . Choosing only molecules made up entirely of structures included in the fitted coefficients, we obtain 7.9 million molecules. The result is a large dataset with a property function that can be exactly calculated and relies on the local graph structure of molecules. Because the groups are only defined in terms of local connectivity, we expect the directed message passing encoding used by our model will be able to fully learn the necessary representation. The only inherent noise is at the level of numerical precision. Accurate representation of experimentally observable enthalpies was not a consideration in the construction of this dataset and would be unnecessary for it to be used in evaluating contributions of different error types. This dataset and models trained from it should not be used for estimation of experimental molecular enthalpies. Our dataset of artificial enthalpy values is available for public download from a Zenodo repository. Models trained on the group additivity dataset were evaluated using a single held out test set comprising 10% of the dataset (790,681 datapoints), chosen randomly. When the number of datapoints used in training are indicated in figures, that is the combined number of datapoints in the training and validation sets, split randomly at a ratio of 80:20. When multiple submodels are combined in an ensemble for the synthetic dataset, the same data splits are used in each submodel. Ensemble submodels are differentiated by beginning training of the model from different random parameter initializations. When the number of datapoints used for training is unspecified, a consistent set of about 0.7 million datapoints is used, corresponding to 10% of the non-test data remaining in the dataset.