  In this work, we extend the SchNet architecture by using weighted skip
connections to assemble the final representation. This enables us to study the
relative importance of each interaction block for property prediction. We
demonstrate on both the QM9 and MD17 dataset that their relative weighting
depends strongly on the chemical composition and configurational degrees of
freedom of the molecules which opens the path towards a more detailed
understanding of machine learning models for molecules.
