Here, we address the absence of inexpensive kinetic measures of chemical stability by investigating whether ML can be used to turn simulation based half-life data into a scalar stressor-specific stability score (Fig. ). The thermal stability of alkanes is taken as an exemplary problem and several model architectures are tested to learn a scalar 'stability score' that allows for the comparison of the relative thermal stability of alkanes. Although one of the simplest organic classes, the resulting scores compress information from theoretical reaction networks spanning billions of reactions. Remarkably, the relative stability is shown to be easily learned by all ML architectures, suggesting the feasibility of greatly generalizing this approach to other stressors and broader chemical classes. The utility of the derived stability score for materials design is finally demonstrated by performing a case-study using a genetic algorithm to discover thermally stable isomers. hydrogen abstraction (used interchangeably referred with H-abstraction in the text), and isomerization (internal H-abstraction). Based on whether the hydrogen is abstracted from the 4th, 5th, 6th, or the 7th nearest neighboring carbon to the radical carbon, isomerization reactions have been named 1-4, 1-5, 1-6, or 1-7 isomerizations (or iso, used interchangeably through the text).