593 citations · 992 across the 23 of their papers we have counts for
8 papers · 1 filter
Validation of uncertainty quantification metrics: a primer based on the consistency and adaptivity concepts
Pascal Pernot
The practice of uncertainty quantification (UQ) validation, notably in machine learning for the physico-chemical sciences, rests on several graphical methods (scattering plots, cal…
Prediction uncertainty validation for computational chemists
Pascal Pernot
Validation of prediction uncertainty (PU) is becoming an essential task for modern computational chemistry. Designed to quantify the reliability of predictions in meteorology, the…
The long road to calibrated prediction uncertainty in computational chemistry
Pascal Pernot
Uncertainty quantification (UQ) in computational chemistry (CC) is still in its infancy. Very few CC methods are designed to provide a confidence level on their predictions, and mo…
Using the Gini coefficient to characterize the shape of computational chemistry error distributions
Pascal Pernot, Andreas Savin
The distribution of errors is a central object in the assesment and benchmarking of computational chemistry methods. The popular and often blind use of the mean unsigned error as a…
Acknowledging user requirements for accuracy in computational chemistry benchmarks
Andreas Savin, Pascal Pernot
Computational chemistry has become an important complement to experimental measurements. In order to choose among the multitude of the existing approximations, it is common to use…
Probabilistic performance estimators for computational chemistry methods: Systematic Improvement Probability and Ranking Probability Matrix. II. Applications
Pascal Pernot, Andreas Savin
In the first part of this study (Paper I), we introduced the systematic improvement probability (SIP) as a tool to assess the level of improvement on absolute errors to be expected…
Probabilistic performance estimators for computational chemistry methods: the empirical cumulative distribution function of absolute errors
Pascal Pernot, Andreas Savin
Benchmarking studies in computational chemistry use reference datasets to assess the accuracy of a method through error statistics. The commonly used error statistics, such as the…
Semi-empirical correction of ab initio harmonic properties by scaling factors: a validated uncertainty model for calibration and prediction
Pascal Pernot, Fabien Cailliez
Bayesian Model Calibration is used to revisit the problem of scaling factor calibration for semi-empirical correction of ab initio harmonic properties (e.g. vibrational frequencies…