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Probabilistic performance estimators for computational chemistry methods: Systematic improvement probability and

Pascal Pernot¹, Andreas Savin²

¹Institut de Chimie Physique, UMR8000, CNRS, Université Paris-Saclay, 91405 Orsay, France.

The Journal of Chemical Physics

|May 3, 2020

Summary

This summary is machine-generated.

This study applies novel statistical indicators, systematic improvement probability, inversion probability (P_inv), and ranking probability (P_r), to assess computational chemistry benchmark quality. These methods evaluate method performance and dataset reliability, particularly with experimental data.

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Area of Science:

Computational Chemistry
Statistical Analysis
Quantum Chemistry

Background:

Assessing computational chemistry method performance is crucial for reliable predictions.
Previous work introduced systematic improvement probability and statistical indicators (P_inv, P_r) to quantify benchmark uncertainties.
Robust statistical methods are needed to address ranking uncertainties in computational chemistry.

Purpose of the Study:

To apply and validate the statistical indicators developed in Paper I on diverse computational chemistry benchmark datasets.
To investigate the utility of error set correlations for assessing benchmark dataset quality.
To provide a robust framework for evaluating the reliability of computational chemistry benchmarks.

Main Methods:

Application of systematic improvement probability, inversion probability (P_inv), and ranking probability matrix (P_r) to nine benchmark datasets.
Analysis of error set correlations to identify potential issues with benchmark data quality.
Utilizing robust statistical techniques for uncertainty quantification in method comparisons.

Main Results:

The statistical indicators were successfully applied across nine diverse benchmark datasets.
Error set correlations revealed valuable insights into the quality and potential biases of benchmark datasets.
The study demonstrated the effectiveness of the proposed indicators in assessing computational chemistry method performance and benchmark reliability.

Conclusions:

The developed statistical indicators provide a robust means to assess computational chemistry benchmark quality and method performance.
Analysis of error correlations offers a valuable complementary approach for evaluating benchmark datasets, especially those using experimental references.
This work enhances the reliability and interpretability of computational chemistry benchmarking studies.