The learning curves further reinforce the advantage of QTAIMenabled models, illustrating a consistent improvement in perfor- mance across varying training set sizes (Figure ). Additionally, the learning curves for both QTAIM and non-QTAIM models do not appear to reach saturation, implying that additional training data could potentially lead to further reductions in prediction errors for both types of models.