22 citations · 51 across the 4 of their papers we have counts for
9 papers
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…
Ions in the Thermosphere of Exoplanets: Observable Constraints Revealed by Innovative Laboratory Experiments
Jérémy Bourgalais, Nathalie Carrasco, Quentin Changeat +7
With the upcoming launch of space telescopes dedicated to the study of exoplanets, the \textit{Atmospheric Remote-Sensing Infrared Exoplanet Large-survey} (ARIEL) and the \textit{J…
Impact of non-normal error distributions on the benchmarking and ranking of Quantum Machine Learning models
Pascal Pernot, Bing Huang, Andreas Savin
Quantum machine learning models have been gaining significant traction within atomistic simulation communities. Conventionally, relative model performances are being assessed and c…
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: Systematic Improvement Probability and Ranking Probability Matrix. I. Theory
Pascal Pernot, Andreas Savin
The comparison of benchmark error sets is an essential tool for the evaluation of theories in computational chemistry. The standard ranking of methods by their Mean Unsigned Error…