In this work, we introduce a new machine learning workflow named AutoBADDIE (Automatic Bonds, Angles, Dihedrals, Dispersion, Impropers, and Electrostatics) which predicts classical interatomic potential parameters for condensed systems by recreating quantum mechanical-derived forces and point charges. AutoBADDIE determines unique chemical environments in input molecules using a message passing algorithm, allowing for autonomous enumeration of discrete environments within a certain graph depth and end-to-end optimization of all the arising interatomic potential parameters. Optimized classical IP parameters are then directly determined through gradient-based optimization from quantum mechanical simulations of molecular clusters.