Among the reducible errors are those associated with how the CG potential energy surface (PES) is modeled (representability errors) and what information is used to parame-terize the coarse-grained PES (information errors). Information errors arise when the training data is insufficient to adequately parameterize the selected CG model (e.g., leading to overfitting or poor performance in certain configurations). In earlier work, we have argued that common CGMD parameterization protocols are overdetermined with respect to typical sources of training data, which we classify as an information error. Information errors can also arise from inadequately sampling the PES (e.g., unintentionally simulating long or broken bonds in the CG model when the PES was only sampled for a fixed bonding configuration). In contrast, representability errors arise when the CG model is insufficiently complex to reproduce the target PES (e.g., modeling anisotropic interactions with isotropic particles). CGMD models have historically relied on simple functional forms due to parameterization challenges. Pair-wise isotropic models with physically motivated bonding and bending terms are by far the most common, although extensive experimentation has occurred with the goal of reducing representability errors. One reason for the excitement surrounding MLbased CG models is that they offer a practical means of eliminating representability errors. Likewise, modern deep neuralnetwork (NN) training algorithms are largely application agnostic, thus these models can be straightforwardly trained on PES data.