In general, the 19 F NMR chemical shifts calculated by the QM method for tranche 2 were better than the chemical shifts predicted by GBT. QM chemical shifts appeared to generalize more effectively. Although the overall trend was towards QM generating less error, comparing QM to GBT on the known and unknown features subsets revealed that GBT was better on the known features subsets and QM was better on the unknown features subset (see Table of the supporting information). This supported our idea that these methods could synergize. Adding QM predictions of the subset with unknown features into the training set for GBT in addition to the LEF4000 outperformed GBT and QM in all metrics as shown in Table . The difference in mean-squared-error (MSE), the parameter most sensitive to strong outliers, between GBT and QM assisted GBT on LEF data was statistically significant in a two-sided t-test (p = 0.008).