SCAN based non-linear double hybrid density functional.

Area of Science:

• Computational chemistry
• Quantum chemistry
• Materials science

Background:

• Density Functional Theory (DFT) is crucial for electronic structure calculations.
• Existing functionals like SCAN have limitations in accuracy for correlated systems.
• Parameter-free functionals are highly desirable for reliable predictions.

Purpose of the Study:

• To develop a novel non-linear and non-empirical (nlane) double hybrid density functional.
• To improve upon the SCAN functional by incorporating accurate adiabatic connection interpolation and asymptotic expansions.
• To achieve accurate energetic predictions without fitted parameters.

Main Methods:

• Accurate interpolation of the adiabatic connection in DFT.
• Incorporation of correct asymptotic expansions.
• Bridging weak correlation and fully interacting limits.

Main Results:

• The nlane-SCAN functional shows improved energetic predictions for moderately and strongly correlated systems.
• It achieves accurate atomic total energies and reaction datasets (GMTKN55 benchmark).
• Outperforms traditional functionals in non-covalent interactions and reduces delocalization errors (SIE4x4, H2+, He2+).

Conclusions:

• nlane-SCAN offers a parameter-free, accurate, and robust method for electronic structure calculations.
• It provides improved performance for various chemical systems, including bond dissociation profiles (H2, N2).
• This development advances the accuracy and reliability of DFT methods.