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Journal Articles The Journal of Chemical Physics Year : 2020

Probabilistic performance estimators for computational chemistry methods: Systematic improvement probability and ranking probability matrix. II. Applications

Abstract

In Paper I [P. Pernot and A. Savin, J. Chem. Phys. 152, 164108 (2020)], we introduced the systematic improvement probability as a tool to assess the level of improvement on absolute errors to be expected when switching between two computational chemistry methods. We also developed two indicators based on robust statistics to address the uncertainty of ranking in computational chemistry benchmarks: Pinv, the inversion probability between two values of a statistic, and Pr, the ranking probability matrix. In this second part, these indicators are applied to nine data sets extracted from the recent benchmarking literature. We also illustrate how the correlation between the error sets might contain useful information on the benchmark dataset quality, notably when experimental data are used as reference.
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Dates and versions

hal-02563582 , version 1 (20-06-2024)

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Pascal Pernot, Andreas Savin. Probabilistic performance estimators for computational chemistry methods: Systematic improvement probability and ranking probability matrix. II. Applications. The Journal of Chemical Physics, 2020, 152 (16), pp.164109. ⟨10.1063/5.0006204⟩. ⟨hal-02563582⟩
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