  In molecular simulations, neural network force fields aim at achieving
\emph{ab initio} accuracy with reduced computational cost. This work introduces
enhancements to the Deep Potential network architecture, integrating a
message-passing framework and a new lightweight implementation with various
improvements. Our model achieves accuracy on par with leading machine learning
force fields and offers significant speed advantages, making it well-suited for
large-scale, accuracy-sensitive systems. We also introduce a new iterative
model for Wannier center prediction, allowing us to keep track of electron
positions in simulations of general insulating systems. We apply our model to
study the solvated electron in bulk water, an ostensibly simple system that is
actually quite challenging to represent with neural networks. Our trained model
is not only accurate, but can also transfer to larger systems. Our simulation
confirms the cavity model, where the electron's localized state is observed to
be stable. Through an extensive run, we accurately determine various structural
and dynamical properties of the solvated electron.
