Models were trained at each iteration using all labeled samples from that iteration and all preceding iterations. Iteration 0 included the same ∼7k alkanes (with a maximum of 15 heavy atoms) as used in the alkane model, but labeled using thermal degradation half-lives generated by RMG. The most notable observation from the pairwise accuracy results (Fig. ) is that accuracies reach saturation within just two active sampling iterations (∼20k labels). This rapid saturation indicates that stability scores are relatively easy to learn within the accuracy range of the ground truth data and that the limiting factor is not data quantity but data quality. Furthermore, adding more data without improving the accuracy of kinetic parameters yields diminishing returns.