2 research outputs found
DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment
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
DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment
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