To summarize, even though the mbGDML models used here only included up to 3-body contributions, they generally predict the liquid structure of water, acetonitrile, and methanol well. Moreover, these force fields automatically include fully flexible molecules and perform no fitting to experimental properties. Further improvements could be made with more expansive training sets and higher-order contributions. For systems without classical parameters, mbGDML can be rapidly trained on relatively small amounts of data and provide valuable dynamical insights for explicitly solvated systems.