Learning Stability Scores from Computationally Generated Kinetic Data

The absence of computational methods to predict stressor-specific degradation susceptibilities represents a significant and costly challenge to the introduction of new materials into applications. Here, a machine-learning framework is developed that predicts stressor-specific stability scores from computationally generated reaction data. Thermal degradation of alkanes was studied as an exemplary system to demonstrate the approach. The half-lives of ~32k alkanes were simulated under pyrolysis conditions using 59 model reactions. Using a hinge-loss function, this half-life data was used to train machine learning models to predict a scalar representing the relative stability based only on the molecular graph. These models were successful in transferability case-studies using distinct train:test splits to recapitulate known stability trends with respect to the degree of branching and alkane size. Even the simplest models showed excellent performance in these case studies, demonstrating the relative ease with which thermal stability can be learned. The stability score is also shown to be useful in a design study, where it is used as part of the objective function of a genetic algorithm to guide the search towards more stable species. This work provides a framework for converting kinetic reaction data into stability scores that provide actionable design information and opens avenues for exploring more complex chemistries and stressors.