This study demonstrates that a small, consistently computed set of computational kinetic data-maintaining uniform relative errors by using the same DFT level of theory-outperforms larger, multi-source datasets, where potential compounding errors can hinder extrapolation to out-of-domain tasks like polymer stability prediction. The ability of small-molecule data to generate meaningful insights for polymer data highlights the potential of using more accessible, less computationally intensive data to predict other complex properties. Using molecular models for data generation can significantly reduce the cost of obtaining data for systems that are otherwise too computationally expensive to study directly and accelerate consistent characterizations across chemical space. This capability creates immediate research opportunities for predicting polymer stability with respect to other stressors and also prediction tasks with molecular analogs beyond polymer stability such as reactivity ratios, adsorption energies, and miscibilities. 4 Methods