  Coarse graining (CG) enables the investigation of molecular properties for
larger systems and at longer timescales than the ones attainable at the
atomistic resolution. Machine learning techniques have been recently proposed
to learn CG particle interactions, i.e. develop CG force fields. Graph
representations of molecules and supervised training of a graph convolutional
neural network architecture are used to learn the potential of mean force
through a force matching scheme. In this work, the force acting on each CG
particle is correlated to a learned representation of its local environment
that goes under the name of SchNet, constructed via continuous filter
convolutions. We explore the application of SchNet models to obtain a CG
potential for liquid benzene, investigating the effect of model architecture
and hyperparameters on the thermodynamic, dynamical, and structural properties
of the simulated CG systems, reporting and discussing challenges encountered
and future directions envisioned.
