The trans conformers occupy a low-uncertainty region, reflecting the model's confidence due to their dominant representation in the training set (90% of the trans conformers were used for training). In contrast, the cis conformers and the geodesic interpolation points exhibit significantly higher uncertainties, indicative of the distribution shift between the training and test datasets. This trend aligns with the model's predictive limitations when applied to underrepresented regions. Figure represents the relationship between the total uncertainty (σ total ) and the absolute prediction error (|y true -y pred |) on a logarithmic scale, categorized into different subsets: Trans-train (dark green), Trans-test (green), Cis (red), and Cis-Trans interpolation (blue). The strong positive correlation between σ total and the prediction error (|y true -y pred |) highlights the reliability of the uncertainty predictions. The trans conformers (both training and test subsets) cluster in the low-error, low-uncertainty region, while the cis conformers and interpolation points dominate the highuncertainty regime. This behavior validates the model's ability to detect regions of low confidence, which is crucial for identifying challenging configurations during active learning.