Evaluating model performance on QM9, we note how our QTAIMenhanced models are able to compete with (and in most metrics beat) the performance of the otherwise best-performing model, Chemprop (Table ). We also augmented Chemprop with QTAIM NCP-only features but here we actually see a slight drop in testing performance. We emphasize that Chemprop does not include vital BCP QTAIM features and thus does not leverage the comprehensive set of QTAIM descriptors. Even here, the performance difference between QTAIM-enabled and non-QTAIM Chemprop models is quite small and also suggests the model is near or at capacity, not that QTAIM features are not informative. Our model, with the inclusion of QTAIM features, is able to outperform all other models on all but 1 task on MAE metrics. Analyzing scatterplots of QM9 test performance, we can also determine the robustness of QTAIM-informed models with few outlier points between predicted and true labels. In addition, we examine the learning curves of our models with and without QTAIM features. To give each set of models even footing, we conducted hyperparameter tuning on models with and without QTAIM features separately and thus these (and overall test performance) correspond to the best models for each descriptor set. We see QTAIM yielding a distinctive improvement in performance in the low data regime with consistent advantages in test performance across all training set sizes (Figure ). Beyond 10,000 structures, however, there is little improvement in test performance of the QTAIM-informed model suggesting the mode is at capacity to generalize or that mainly irreducible errors remain.