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Scaling dynamical correlation energy from density functional theory correlation functionals.

B Ramachandran¹

¹Chemistry, College of Engineering and Science, Louisiana Tech University, Ruston, Louisiana 71272, USA. ramu@latech.edu

The Journal of Physical Chemistry. A

|January 13, 2006

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Scaling density functional theory (DFT) correlation energy functionals improves molecular atomization energies. Molecule-specific scale factors achieve near-perfect agreement with experimental data without compromising other molecular properties.

Area of Science:

Computational chemistry
Quantum chemistry

Background:

Density functional theory (DFT) correlation functionals, particularly LYP, capture the dynamical correlation energy in molecules.
Existing DFT methods may require adjustments for optimal accuracy in predicting molecular properties.

Purpose of the Study:

To investigate the scaling of dynamical correlation energy in DFT.
To determine if scaling DFT correlation functionals can enhance accuracy in atomization energies without negatively impacting structural and spectroscopic properties.

Main Methods:

The study employed a method similar to scaled external correlation, focusing on scaling DFT correlation functionals.
Four DFT functionals (BLYP, OLYP, B3LYP, O3LYP) were applied to 19 molecules, including the AE6 database.
Molecule-specific scale factors were derived and tested.

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Main Results:

Nearly perfect agreement with experimental atomization energies was achieved using molecule-specific scale factors.
The scaling approach did not increase average errors in other molecular properties compared to standard DFT calculations.
Optimal scale factors reduced the mean unsigned error per bond to levels competitive with multilevel multicoefficient methods.

Conclusions:

Scaling DFT correlation energy functionals is an effective strategy for improving atomization energy predictions.
This method offers a way to achieve high accuracy in thermochemistry without sacrificing other crucial molecular characteristics.
The findings suggest a practical approach to enhance the predictive power of DFT for molecular energetics.