  Atomic partial charges are crucial parameters in molecular dynamics (MD)
simulation, dictating the electrostatic contributions to intermolecular
energies, and thereby the potential energy landscape. Traditionally, the
assignment of partial charges has relied on surrogates of \textit{ab initio}
semiempirical quantum chemical methods such as AM1-BCC, and is expensive for
large systems or large numbers of molecules. We propose a hybrid physical /
graph neural network-based approximation to the widely popular AM1-BCC charge
model that is orders of magnitude faster while maintaining accuracy comparable
to differences in AM1-BCC implementations. Our hybrid approach couples a graph
neural network to a streamlined charge equilibration approach in order to
predict molecule-specific atomic electronegativity and hardness parameters,
followed by analytical determination of optimal charge-equilibrated parameters
that preserves total molecular charge. This hybrid approach scales linearly
with the number of atoms, enabling, for the first time, the use of fully
consistent charge models for small molecules and biopolymers for the
construction of next-generation self-consistent biomolecular force fields.
Implemented in the free and open source package \texttt{espaloma\_charge}, this
approach provides drop-in replacements for both AmberTools \texttt{antechamber}
and the Open Force Field Toolkit charging workflows, in addition to stand-alone
charge generation interfaces. Source code is available at
\url{https://github.com/choderalab/espaloma_charge}.
