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A method to estimate statistical errors of properties derived from charge-density modelling.

Bertrand Fournier1, Benoît Guillot2, Claude Lecomte2

  • 1Institut Galien Paris Sud, UMR CNRS 8612, Université Paris Sud, Faculté de Pharmacie, Université Paris-Saclay, 5 rue Jean-Baptiste Clément, Châtenay-Malabry, 92296, France.

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Summary
This summary is machine-generated.

A new method estimates uncertainties in charge-density models by calculating sample standard deviations (SSD) from varied models. This approach provides reliable error estimates for various properties, enhancing crystallographic analysis.

Keywords:
Monte Carlo methodselectron densityintermolecular interactionstopologyuncertainty

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Area of Science:

  • Crystallography
  • Quantum Chemistry
  • Materials Science

Background:

  • Estimating uncertainties in charge-density models is complex.
  • Accurate uncertainty quantification is crucial for reliable property derivation.

Purpose of the Study:

  • To introduce a robust methodology for estimating uncertainties in charge-density model properties.
  • To implement this methodology within the MoPro software for practical application.

Main Methods:

  • A sample standard deviation (SSD) methodology was developed.
  • Randomly deviating charge-density models were generated respecting the variance-covariance matrix from least-squares refinement.
  • The procedure was applied to estimate uncertainties for topological properties, atomic charges, electrostatic potentials, and interaction energies.

Main Results:

  • The SSD methodology successfully estimates uncertainties for various properties derived from charge-density models.
  • The method was exemplified using the charge density of (E)-5-phenylpent-1-enylboronic acid.
  • The procedure is implemented in the freely available MoPro program.

Conclusions:

  • The proposed SSD methodology offers a straightforward and reliable way to quantify uncertainties in charge-density modeling.
  • This advancement facilitates more accurate interpretation of chemical properties and interactions from crystallographic data.