Beyond accuracy, we were also interested in improving the efficiency of the on-the-fly sampling, which seemed achievable considering the generally good accuracy of the cNNP trained on CSP minima, implying that only a small number of structures with high uncertainties need to be added to the training set to yield a robust potential. To investigate this we repeated the first on-the-fly training starting from the same 10 structures but with a higher uncertainty threshold of 10 kJ mol -1 . This change resulted in only 91 structures being added to the training set, a reduction of 95% compared to the on-the-fly training with a 2 kJ mol -1 threshold. Despite the smaller training set, the model performs well. The correlation plots of this model (Figure ) show it still achieves a significant improvement compared to the model trained only on CSP lattice energy minima, and importantly, eliminates the outliers, which suggests a robust description of the energy surface. Of course, the average errors are not as small as with the 2 kJ mol -1 uncertainty threshold, but considering the reduced computational cost, it could be a worthwhile compromise, allowing longer, higher energy trajectories and/or more trajectories during the on-the-fly training.