An array of four model types and seven train:validation:test splits were used to train possible DFTlevel models for predicting stability using ROBERT (Figure ). We selected the highestperforming model architecture after default PFI analysis based on the root mean squared error (RMSE) values of the validation set, a multivariate linear regression (MLR) employing a 101:18:13 train:validation:test split (Figure ). PFI scores were then further analyzed to limit model training to only the eight most important physical properties of the NCBs. This number of descriptors was selected because it was the lower limit at which we still had high model performance (Table ). More details regarding model training can be found in section 7 of the supplemental information. This analysis led to a high-performing model, shown by a high correlation (test set R 2 = 0.98), as well as mean absolute error (MAE) and RMSE within DFT accuracy levels (≤ 3 kcal/mol, 12.552 kJ/mol). descriptors. In addition to the standard model assessment performed in ROBERT, we also tested our model performance with a Spearman correlation, which a gave rank-order correlation of 0.974, demonstrating the ability of our model to correctly order NCBs with respect to their relative stabilities (Table ). This feature is ideal for an optimization protocol, where only the most stable NCBs are considered for experimental validation, so getting the correct stability ranking is as effective as predicting the actual value. We also explored a model trained with semi-empirical descriptors in an attempt to reduce the computational cost of making predictions, but the difference in performance relative to the cost of each method was not enough to favor the semi-empirical model (see section 7 of the supplemental information).