Plane-wave pseudopotential implementation and performance of SCAN meta-GGA exchange-correlation functional for...

Area of Science:

• Condensed matter physics and chemistry
• Computational materials science
• Quantum chemistry

Background:

• Density Functional Theory (DFT) is a cornerstone for materials science.
• Meta-generalized gradient approximations (meta-GGAs) offer enhanced accuracy over GGAs.
• The Strongly Constrained and Appropriately Normed (SCAN) meta-GGA is a promising exchange-correlation functional.

Purpose of the Study:

• Implement and evaluate the SCAN meta-GGA exchange-correlation functional within the planewave-pseudopotential (PW-PP) formalism.
• Assess the performance of the SCAN functional in the PW-PP scheme for diverse condensed matter applications.
• Identify potential numerical sensitivities and limitations of the SCAN functional in this computational framework.

Main Methods:

• Implementation of the SCAN meta-GGA functional using the Troullier-Martins pseudopotential scheme within the PW-PP formalism.
• Application of the developed PW-PP-SCAN method to crystalline Si and Ge, phosphorene phase transitions, and water molecule adsorption on graphene.
• Analysis of computational cost and accuracy compared to GGA and hybrid functionals.

Main Results:

• The SCAN functional demonstrates improved accuracy over GGA functionals for the tested condensed matter systems.
• The PW-PP implementation of SCAN shows a notable dependence on the fast Fourier transform grid density.
• Phase transition energetics calculations can be sensitive to the level of theory used for pseudopotential generation.

Conclusions:

• The SCAN meta-GGA functional is a promising tool for DFT calculations in condensed matter, offering a good balance of accuracy and computational cost.
• Careful convergence testing with respect to the fast Fourier transform grid and judicious choice of pseudopotentials are essential for reliable SCAN calculations.
• Further development may be needed to mitigate numerical sensitivities and ensure robust performance across various applications.