Five different models are trained with different random splits (train test split from scikit-learn with different random states) of the training data into training (80%) and validation (20%) sets. The test set RMSE ranges between 0.55-0.65 eV (Table ). Here we focus on a single set of uncertainties (for the model trained with random seed 42), but results for the remaining models can be found in the supporting information. As has been the case in other studies comparing uncertainty evaluation metrics across different models and data sets, the observed magnitude of the evaluation metrics change from what we observed for the logP dataset (Tables and). While the linear fit for the RMSE/RMV correlation is decent (R 2 = 0.83), the observed slope of 0.27 is much too low and the intercept of 0.35 eV too high. Again we turn to the error-based calibration plots with bootstrapped 95% confidence intervals to get some more insight into the performance of the uncertainty estimates (Figure ). The lower uncertainties are quite well-calibrated, but the higher uncertainties are greatly overestimated. The Spearman's rank correlation coefficient (ρ rank = 0.41) is higher than what we observed for any of the uncertainty estimates for the logP data. Meanwhile, ρ sim rank is also much higher (ρ sim rank = 0.56 ± 0.05) reflecting the uncertainty distribution being much wider in terms of the model RMSE. The fact that the simulated value is higher than the observed one, makes sense based on the error-based calibration plots; the difference We see that the non-zero miscalibration area is mostly caused by an increased number of low errors compared to what is expected from the uncertainty-distribution. This is also clear from the error-based calibration plot, where we observe that it is the errors for the high uncertainties that are significantly lower than what is expected from the corresponding uncertainties, i.e. these uncertainties are overestimated.