Chemprop includes a variety of popular uncertainty estimation, calibration, and evaluation tools. The estimation methods include deep ensembles 77 , dropout , mean-variance estimation (MVE) 71 , and evidential 72 , as well as a special version of ensemble variance for spectral predictions , and the inherently probabilistic outputs of classification models. After estimating the uncertainty in a model's predictions, it is often helpful to calibrate these uncertainties to improve their performance on new predictions. We provide four such methods for regression tasks (z-scaling 79 , t-scaling, Zelikman's CRUDE, and MVE weighting )