  We present a robust and computationally efficient approach for assigning
partial charges of atoms in molecules. The method is based on a hierarchical
tree constructed from attention values extracted from a graph neural network
(GNN), which was trained to predict atomic partial charges from accurate
quantum-mechanical (QM) calculations. The resulting dynamic attention-based
substructure hierarchy (DASH) approach provides fast assignment of partial
charges with the same accuracy as the GNN itself, is software-independent, and
can easily be integrated in existing parametrization pipelines as shown for the
Open force field (OpenFF). The implementation of the DASH workflow, the final
DASH tree, and the training set are available as open source / open data from
public repositories.
