As previously shown, SQM calculations can, when coupled with ML, provide DFT-accuracy reaction barrier predictions for ds1-3. To further show the value of our hybrid SQM-ML approach, we first built models to predict the DFT reaction barriers for the new dataset (ds4). To provide a fair representation of model performance, five random seeds (RS) were tested for each model throughout this work (full results for ds1 and ds4 reaction barrier predictions are averaged and can be found in the ESI, section 4.3, Table and S7 respectively). The best performing model for ΔG ‡ prediction on ds4 is SVR, which achieved an averaged test set MAE of 0.97 ± 0.13 kcal mol -1 which is below the 1 kcal mol -1 accuracy threshold.