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Uncertainty quantification (UQ) in computational chemistry (CC) is still inits infancy. Very few CC methods are designed to provide a confidence level ontheir predictions, and most users still rely improperly on the mean absoluteerror as an accuracy metric. The development of reliable uncertaintyquantification methods is essential, notably for computational chemistry to beused confidently in industrial processes. A review of the CC-UQ literatureshows that there is no common standard procedure to report nor validateprediction uncertainty. I consider here analysis tools using concepts(calibration and sharpness) developed in meteorology and machine learning forthe validation of probabilistic forecasters. These tools are adapted to CC-UQand applied to datasets of prediction uncertainties provided by compositemethods, Bayesian Ensembles methods, machine learning and a posterioristatistical methods.

The long road to calibrated prediction uncertainty in computational chemistry