  Coarse-graining is a molecular modeling technique in which an atomistic
system is represented in a simplified fashion that retains the most significant
system features that contribute to a target output, while removing the degrees
of freedom that are less relevant. This reduction in model complexity allows
coarse-grained molecular simulations to reach increased spatial and temporal
scales compared to corresponding all-atom models. A core challenge in
coarse-graining is to construct a force field that represents the interactions
in the new representation in a way that preserves the atomistic-level
properties. Many approaches to building coarse-grained force fields have
limited transferability between different thermodynamic conditions as a result
of averaging over internal fluctuations at a specific thermodynamic state
point. Here, we use a graph-convolutional neural network architecture, the
Hierarchically Interacting Particle Neural Network with Tensor Sensitivity
(HIP-NN-TS), to develop a highly automated training pipeline for coarse grained
force fields which allows for studying the transferability of coarse-grained
models based on the force-matching approach. We show that this approach not
only yields highly accurate force fields, but also that these force fields are
more transferable through a variety of thermodynamic conditions. These results
illustrate the potential of machine learning techniques such as graph neural
networks to improve the construction of transferable coarse-grained force
fields.
