Screened hybrid meta-GGA exchange-correlation functionals for extended systems.

Area of Science:

• Computational Materials Science
• Quantum Chemistry
• Solid-State Physics

Background:

• Screened Hartree-Fock exchange integrated with semilocal functionals accurately predicts solid-state properties by incorporating non-locality.
• Constructing screened exchange-correlation functionals requires careful design of the short-range semilocal functional, where the exchange hole is crucial.
• Existing generalized gradient approximation (GGA)-based hybrid functionals like HSE06 provide a benchmark for solid-state property predictions.

Purpose of the Study:

• To propose a novel meta-generalized gradient approximation (meta-GGA) level screened hybrid functional.
• To assess the performance of the new functional for predicting key solid-state properties.
• To compare the proposed functional against the established GGA-based hybrid functional HSE06.

Main Methods:

• Development of a meta-GGA screened hybrid functional incorporating a local density approximation-based exchange hole and the Tao-Mo semilocal functional.
• Extensive assessment of the newly proposed functional's accuracy for predicting lattice constants, bulk moduli, band gaps, and cohesive energies.
• Comparative analysis of the new functional's results against those obtained using the GGA-based hybrid functional HSE06.

Main Results:

• The proposed meta-GGA screened hybrid functional demonstrates proficiency in predicting various solid-state properties.
• Lattice constants, bulk moduli, band gaps, and cohesive energies were accurately calculated using the new functional.
• The new functional shows potential for substantial improvements over GGA-based hybrid functionals for certain solid-state properties.

Conclusions:

• The developed meta-GGA screened hybrid functional offers a promising advancement in accurately predicting solid-state properties.
• Moving beyond GGA approximations can lead to significant improvements in the prediction of material characteristics.
• The proposed functional provides a valuable tool for computational materials science research.