Furthermore, the QM9 dataset was used as a lownoise real-world dataset. We selected the enthalpy at 298 K and internal energy at 0 K and 298 K as sizeextensive properties, as well as the HOMO-LUMO gap as a size-intensive property. We trained directly on the quantum chemical energies, without subtraction of the atomic reference values. A single held out test set comprising 10% of the data (13,083 datapoints) was used. The rest of the data was used for the training and validation set, where a specified number of datapoints were selected randomly and split into training and validation sets in ratios of 80:20. To compute learning curves using QM9, i.e. the model performance dependent on dataset size, differently-sized training and validation sets were drawn containing a specified number of datapoints N , while leaving the test set untouched. Submodels in an ensemble using the QM9 data share the same data splits with different initial model parameters.