We evaluate neural network coarse-grained force fields compared to traditional CG molecular mechanics force fields. We conclude neural network force fields are able to extrapolate and sample from unseen regions of the free energy surface free energy surfaces when trained with limited data. Our results come from 66 trained force fields trained on different combinations of clustered free energy surfaces across three proteins. We used total variation similarity as our metric, which assesses agreement between free energy surfaces of force fields. Additionally, force matching error was found to only be weakly correlated with a force field's ability to reconstruct the correct free energy surface. These conclusions support the common hypothesis that constructing force fields on one region of the protein free energy surface can extrapolate well.
Additional details on model training hyperparameters