Table summarizes the performance of three uncertainty quantification (UQ) methods (ensemble, evidential, and mean-variance estimation (MVE)) selected from Chemprop's available UQ options. For the evidential uncertainty, we used the total uncertainty (the sum of aleatoric and epistemic components). We trained all models on the gap values from the QM9 dataset and calibrated the predictions using the z-scaling method with the standard deviation as the regression calibrator metric. We then evaluated the methods based on four metrics: negative log likelihood (NLL), Spearman rank correlation (ρ), expected normalized calibration error (ENCE), and miscalibration area (MA). On this task, we observe that MVE performs the best across all four metrics, while ensemble performs the worst across all metrics, with evidential in between. However, we emphasize that UQ performance can vary depending on task, dataset size, representation, and other factors. The results presented here simply serve as an illustration of Chemprop UQ capabilities; we refer readers to substantial UQ benchmarking work done previously for further discussion of the merits and pitfalls of various methods and metrics on chemical and materials data.