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.
@article{arxiv.1810.09751,
title = {Analysis of Atomistic Representations Using Weighted Skip-Connections},
author = {Kim A. Nicoli and Pan Kessel and Michael Gastegger and Kristof T. Schütt},
journal= {arXiv preprint arXiv:1810.09751},
year = {2018}
}
Comments
NIPS 2018 Workshop: Machine Learning for Molecules and Materials