Besides model size, there are also other factors contributing to model bias, such as molecular representations and model architectures. We explore the effects of architecture and representation by comparing the performance of a d-MPNN to SchNet. Message passing neural networks are built on 2-dimensional graph representations, whereas SchNet takes the 3-dimensional coordinates as input. We therefore expect SchNet to perform better for targets that depend on the 3-dimensional conformation, such as the enthalpy in the QM9 dataset.