Across both tasks (first and second vertical excitation energies) QTAIM-enabled models were the top-performing algorithms (Table 3). Chemprop with QTAIM and our models with QTAIM yielded improved test errors over all other models with a notable gap in performance between QTAIM/ML models and all others. Again, we note that Chemprop's QTAIM featurization was limited to only QTAIM NCP features, and even then, this led to increased performance. Finally, when examining predicted versus true plots of our models, it becomes evident that QTAIM-enhanced models exhibit greater robustness, displaying fewer outlier residual errors compared to their non-QTAIM equivalent.