Despite their prevalence, CGMD simulations have yet to become a routine tool for molecular and material design applications because of their relatively bespoke implementation. CGMD model parameterization varies depending on the subset of properties being targeted for CG reproduction, the training data available, and the targeted model complexity, among other factors. For this reason, several prominent researchers in the field have quite laudably labored to democratize CGMD parameterization procedures (e.g., see the BOCS , VOTCA 11 , DeePCG , IBIsCO , MagiC , MSCGFM packages). A major challenge for these efforts is that CGMD methods development is still very active, and so there is no generic black-box implementation currently available that could play a similar role for materials modeling as density functional theory (DFT) plays in many molecular screening workflows. The recent advent of machine-learning (ML) based CGMD models has created an attractive pathway to such a black-box implementation, albeit still at the proof of principle stage. In particular, little benchmarking has been done to demonstrate the relative advantage of these more com-plex ML-based potentials over much simpler physics-based models, and whether the latter might be used to improve the former. Addressing these gaps is the main focus of this work.