There are several avenues for further improving this work. The current work focuses on the thermal stability of alkanes as an entry point for the broader range a degradation suscpetibilities that are relevant to materials development. We found that both machine learning architectures were easily able to learn the thermal stability structure-function relationships such that the stability scores could be reliably used in design tasks. Nevertheless, the generalization to other chemistries and stressors is both obvious and necessary. The ease with which the present relationships were learned suggests that the broad data generation performed here so that the scaffolded training:testing case-studies could be performed will not be necessary when considering other chemistries and stressors. As such, we do not anticipate fundamental data generation obstacles to extending this approach to redox stability (e.g., by considering reactions involving anions and cations of neutral parents) or other chemistries beyond alkanes. We further showed that the kinetic data contains material stability information and that this relative information can be learned by neural networks and even be extrapolated to unseen data for material design. This means that if properly defined, kinetics can be learned in a way useful to computational material design and virtual screening.