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Accurate Electron Densities at Nuclei Using Small Ramp-Gaussian Basis Sets.

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New mixed ramp-Gaussian basis sets accurately predict electron densities at nuclei. These R-31G basis sets outperform traditional all-Gaussian sets, offering significant advantages for computational chemistry.

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Area of Science:

  • Computational Chemistry
  • Quantum Chemistry
  • Theoretical Chemistry

Background:

  • Accurate calculation of electron densities at nuclei is crucial in quantum chemistry.
  • All-Gaussian basis sets struggle with the electron-nuclear cusp, limiting accuracy.
  • Existing basis sets often require specialized modifications for precise nuclear electron density prediction.

Purpose of the Study:

  • To introduce and evaluate novel mixed ramp-Gaussian basis sets for improved electron density calculations.
  • To assess the performance of the R-31G basis set in predicting electron densities at nuclei.
  • To compare the R-31G basis set against established all-Gaussian basis sets.

Main Methods:

  • Development of mixed ramp-Gaussian basis sets, exemplified by R-31G, incorporating ramp functions.
  • Modeling the R-31G basis set on the widely used 6-31G all-Gaussian basis set.
  • Comparative analysis of electron density prediction accuracy for elements Li-F using R-31G versus other basis sets.

Main Results:

  • The R-31G basis set accurately captures the electron-nuclear cusp, a feature absent in all-Gaussian sets.
  • R-31G basis sets demonstrate superior performance in predicting nuclear electron densities compared to triple-ζ or lower quality all-Gaussian sets.
  • The accuracy of R-31G is comparable to specialized basis sets like pcJ-0, designed for nuclear electron density calculations.

Conclusions:

  • Mixed ramp-Gaussian basis sets offer significant advantages over traditional all-Gaussian basis sets for calculating electron densities at nuclei.
  • The R-31G basis set represents a robust and accurate general-purpose tool for electronic structure calculations.
  • These findings pave the way for more reliable computational predictions in areas sensitive to nuclear electron density.