Abstract
The SCAN meta-generalized gradient approximation (GGA) functional is known to describe multiple properties of various materials with different types of bonds with greater accuracy, compared to the widely used PBE GGA functional. Yet, for alkali metals, SCAN shows worse agreement with experimental results than PBE despite using more information about the system. In the current study, this behavior for alkali metals is explained by identifying an inner semicore region which, within SCAN, contributes to an underbinding. The inner semicore push toward larger lattice constants is a general feature but is particularly important for very soft materials, such as the alkali metals, while for harder materials the valence region dominates.
Highlights
The most common theoretical approach to calculating the properties of solids and molecules is Kohn-Sham density functional theory (KS-DFT).1 The main accuracy restricting factor of this method is the functional form of the exchange-correlation energy, Exc
The step of functional development was to add a functional dependence on the gradient of the density. This led to the generalized gradient approximations (GGAs),2,3 which have better accuracy in multiple cases
We have analyzed in detail the results obtained with the MGGA functional SCAN for the alkali metals
Summary
The most common theoretical approach to calculating the properties of solids and molecules is Kohn-Sham density functional theory (KS-DFT). The main accuracy restricting factor of this method is the functional form of the exchange-correlation energy, Exc. Where exc is the exchange-correlation energy per volume unit and is a function of local electronic properties, such as the electron density, electron density gradient, or kinetic-energy density (KED). The step of functional development was to add a functional dependence on the gradient of the density. This led to the generalized gradient approximations (GGAs), which have better accuracy in multiple cases. In the meta-GGA (MGGA) functionals, the KED and/or the Laplacian of the density are used in the parameterization of exc. Several MGGAs with different constraints and goals have been developed (see Ref. 4 for a review), and benchmarks of these different functionals have shown how MGGAs can improve the overall accuracy compared to GGAs.. Several MGGAs with different constraints and goals have been developed (see Ref. 4 for a review), and benchmarks of these different functionals have shown how MGGAs can improve the overall accuracy compared to GGAs. The improved performance can, depending on the point of view, be related to the MGGAs being able to distinguish more bonding situations, better fit reference data, or satisfy more exact constraints.
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