PaiNN employs message passing for 3D-embedded graphs, where the molecular representation is constructed on the fly for each graph node (atom) based on its surrounding environment (by default 5 Å). Atomic features are updated through a sequence of interaction layers, which, followed by an atom-centered neural network, allows modeling of the final property of interest (e.g., energy). For a more detailed description, we recommend consulting the original reference. The PaiNN model is implemented in the SchNetPack When training a model with multiple properties, it is common to use a loss function combining the MAEs of all the model properties. In the case of energies and forces, our loss function is