for both decomposition and catalytic reduction, allowing this set of descriptors to be used in a future multi-objective optimization for both stability and reduction potential. Permutation feature importance (PFI) analysis was implemented to isolate and use only the most important descriptors for NCB stability to help prevent model overfitting. PFI begins with a model trained on all descriptors, then systematically permutates one descriptor at a time to determine the impact of that descriptor on the overall model. The worse a model performs when a descriptor is permutated, the more important that descriptor is to making predictions. This analysis is automated in ROBERT, resulting in a PFI score for each descriptor (Table ). The eight descriptors with the highest PFI scores were used in our model, including atomic charges at N1, C2, C3, C5, and C4H and electrophilic condensed Fukui indices at C3, R2, and C4H. We also performed a comparison of these properties calculated from the lowest energy conformer, which was used in model training, with the Boltzmann weighted properties for our representative subset of 34 NCBs. The maximum differences between values for all atomic charges and electrophilic condensed Fukui indices of these NCBs were 0.001 and 0.008, respectively. The differences are low relative to the magnitude of the values, suggesting we do not need to include the full conformational ensemble for our descriptors.