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A Critical Evaluation of the Hybrid KS DFT Functionals Based on the KS Exchange-Correlation Potentials
Vignesh Balaji Kumar1, Szymon Śmiga1, Ireneusz Grabowski1
1Institute of Physics, Faculty of Physics, Astronomy, and Informatics, Nicolaus Copernicus University in Toruń, ul. Grudzia̧dzka 5, 87-100 Toruń, Poland.
We developed a method to evaluate hybrid functionals for density functional theory (DFT). High Hartree-Fock exchange mixtures yield better Kohn-Sham potentials and ionization potentials, guiding new functional development.
Area of Science:
- Quantum Chemistry
- Computational Materials Science
- Theoretical Chemistry
Background:
- Density Functional Theory (DFT) is a cornerstone of modern computational chemistry and materials science.
- Hybrid exchange-correlation (XC) density functional approximations (DFAs) offer improved accuracy but require rigorous evaluation.
- Assessing the quality of hybrid DFAs necessitates fundamental physical quantities.
Purpose of the Study:
- To establish a critical methodology for evaluating hybrid XC-DFAs.
- To assess the quality of 155 hybrid DFAs using fundamental properties.
- To identify key characteristics of high-performing hybrid XC-DFAs.
Main Methods:
- Developed a novel methodology to evaluate hybrid XC-DFAs.
- Calculated XC potentials by inverting self-consistent Kohn-Sham (KS) electron densities.
- Tested 155 hybrid DFAs against Full Configuration Interaction (FCI) and Coupled Cluster (CCSD(T)) reference data.
Main Results:
- A subset of hybrid functionals, particularly those with high Hartree-Fock exchange, yield accurate KS XC potentials.
- The quality of the calculated ionization potential (IP) is strongly correlated with the accuracy of the XC potential.
- XC energy calculations are significantly influenced by functional-driven errors, impacting electron densities.
Conclusions:
- High Hartree-Fock exchange content is crucial for developing accurate hybrid XC-DFAs.
- The developed methodology provides a new framework for constructing improved KS-DFT functionals.
- This work offers valuable insights for the future design of more reliable computational chemistry tools.
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