Domain-informed graph neural networks: a quantum chemistry case study. (arXiv:2208.11934v1 [cs.LG])

We explore different strategies to integrate prior domain knowledge into the
design of a deep neural network (DNN). We focus on graph neural networks (GNN),
with a use case of estimating the potential energy of chemical systems
(molecules and crystals) represented as graphs. We integrate two elements of
domain knowledge into the design of the GNN to constrain and regularise its
learning, towards higher accuracy and generalisation. First, knowledge on the
existence of different types of relations (chemical bonds) between …

arxiv case case study chemistry graph graph neural networks lg networks neural networks quantum study