As before, all models were tested over five random states with results averaged. For context, three of the pre-ML AM1-DFT MAEs are large, especially for the interaction energies (Table ). The averaged test MAEs from the ds2 ML models for each target, across both datasets, were significantly lower than the pre-ML AM1-DFT MAEs which shows that the models can make impressive predictions close to the 1 kcal mol -1 accuracy threshold on unseen datapoints. Noticeably, the interaction energy ML predictions across both datasets were impressive with the MAE on the cycloalkenone data dropping from a pre-ML value of 12.71 to 1.52 kcal mol -1 . As we have previously shown, SQM TSs can be a very good approximation to DFT TSs for cycloadditions. Therefore, our hybrid SQM-ML approach gives energies and mechanistic insight close to the accuracy of DFT at a fraction of the computational cost.