Chemprop is a flexible framework for computing a host of different molecule-level and reaction-level properties . The algorithm incorporates a local embedding from atom/bond features, a graph-level embedding function that transforms finalized representation graphs to a fixed-size vector, and a standard feedforward neural network for property prediction. We adopted our QTAIM generator to construct atom-level QTAIM features in a format compatible with Chemprop's featurization. Here we limit ourselves to atom-level molecular features, excluding both bonds and reactions due to the inflexibility of Chemprop for user-defined bonds and the added complexity of atom-mappings. Hyperparameter optimization was performed using their convenient bayesian optimization functionalities.