Inexpensive measures of stability are scarce despite the central role played by establishing degradation susceptibilities in the materials translation process. Developments in automated reaction prediction provide access to diverse reaction data that is relevant to predicting such stability behaviors but nevertheless needs to be generalized across conserved chemical structures. The current works shows how computationally generated kinetic data can be generalized into scalar stability scores using relatively simple machine learning architectures.