In Fig. (right), we furthermore observe that the enthalpy which largely depends on the 3-dimensional conformation can be modeled by a 3-D approach in much greater detail. However, a direct comparison is difficult since SchNet not only differs in the general architecture, but also in the way the model is initialized. Namely, SchNet utilizes the mean and standard deviation of average atomic contributions to the target properties in the training set to initialize the model with a good guess of the target property of each molecule. This is especially helpful for extensive properties since it enforces additivity of the atomic contributions. As such, d-MPNN and SchNet are not directly comparable, since the d-MPNN has to explicitly learn the additivity from the data.