A dataset of experimental degradation temperatures for small molecules was used to test the extrapolative power of stability score models trained on molecules with up to 15 heavy atoms (HA). The experimental mean absolute error (MAE) in this dataset was 35 • C. Of the total 7k data points, more than 6k contain over 15 heavy atoms (Fig. ), making this a reasonable test of the predictive models' extrapolative capabilities. These are independently curated from the polymer data, but the measurement technique is most closely compared with T d (onset of degradation). The small molecules also span an extensive range of degradation temperatures, from 0 • C to 450 • C with most lying between 150 • C and 300 • C (Fig. ). From the summary of the atom and connectivity distribution for this dataset (Fig. ), we observe a clear prominence of unsaturated carbon atoms bonded to each other-especially sp 2 hybridized, aromatic, and ring carbons. This chemical diversity presents a deliberate challenge for our models, which are primarily trained on simpler structures. Similar to the polymer database, this dataset was also divided into five categories for pattern analysis: i) all, ii) acyclic, iii) CHON (containing at least one oxygen or nitrogen atom, and no chlorine or fluorine atoms), iv) ≤ 15 HA, and v) ≥ 15 HA. Two computational datasets were used for model training, while experimental data was solely used for accuracy calculations. The first dataset was taken from our earlier work focused on predicting alkane stability scores from first-principles kinetic data (called the alkane stability paper in further discussion). The study provided a systematic approach for extracting relative stability information from alkane kinetics. For model training and analysis in this main text, we extracted thermal degradation half-life data for acyclic alkanes with up to 15 heavy atoms (∼7k labels)