The second dataset used in this study was generated using Reaction Mechanism Generator (RMG), which incorporates a wide range of reaction templates and learned reaction rate parameters from extensive experimental and computational literature (called the literature data in further discussion). A corpus of ∼750k acyclic C, H, O, N, F, and Cl-containing molecules with up to 15 heavy atoms (HA) was extracted from PubChem. To balance computational costs while maximizing learnable information, an ensemble active learning approach (Fig. ) was employed to sample and label molecules from this corpus. In the initial iteration (iteration 0), acyclic alkanes with up to 15 HA were selected from the first dataset, and their thermal degradation half-lives were calculated using RMG. In each subsequent iteration, all labeled data was used to train an ensemble of 30 multi-layer perceptron (MLP) models to minimize the mean squared error (MSE) of half-life prediction. The ensemble then predicted the unlabeled data, and the top 8k molecules with the highest ensemble variance were selected for further labeling. After 10 iterations, a dataset of approximately 57k molecules was generated. Although a significant part of this dataset comprises saturated carbons bonded to hydrogens (Fig. ), it is still more complex and descriptive than the alkane dataset owing to the presence of a substantial number of sp 2 carbons along with oxygen, nitrogen, chlorine, and fluorine atoms. Also, on only accounting for heavy atoms, the percentage elemental compositions become more descriptive: sp