The left panel of Fig. depicts the mean absolute errors of a d-MPNN and SchNet for HOMO-LUMO gaps. The 3-D method (SchNet) needs more training data to perform well than the simpler 2-D method (d-MPNN) but provides better performance with very large data set sizes. To choose the best model for a given data set, it is therefore advisable to take into account the size and diversity of the data. For small (or highly diverse and sparse) data sets, a simpler model is often preferred. Besides the general model architecture, many smaller details and hyperparameters largely influence model performance, too. We showcase this effect by examining the influence of the aggregation function that combines atomic into molecular representations or properties. Both d-MPNN and SchNet first compute vectors of properties for each atom in a molecule, and then combine these atom vectors to construct a single fixed-length learned-fingerprint vector for the molecule. This vector is the input to a conventional feed-forward neural-net in the d-MPNN, or directly produces the target within SchNet. However, what is the best way to combine the atom vectors, by averaging or summing? In Fig. (left), one can see that either method of combination works about equally well for predicting HOMO-LUMO gaps. But in Fig. (right), the "sum" method works much better than the "avg" method. This is because enthalpy is an extensive quantity, that increases more or less linearly with the number of atoms. If one averages (rather than sums) the atom vectors, one loses information about how many atoms are in the molecule. In contrast, the HOMO-LUMO gap has a much weaker dependence on molecular size, it is more like an intensive quantity, so it can be modeled using "avg" about as well as it is modeled using "sum". An extensive (size-conserving) representation and architecture is therefore essential for size-extensive properties like the energy. However, it can be easily overlooked when training models, especially when training multi-task models for a mixed set of extensive and intensive targets such as the QM9 dataset which contains both. As visible in Fig. , choosing an intensive architecture (averaging over all atoms) for an extensive property such as the enthalpy leads to large performance losses for both the d-MPNN and SchNet. For an intensive property, there is nearly no difference in performance, so we recommend using extensive representations and architectures when in doubt.