The local and semilocal exchange-correlation functionals depend locally on quantities like the electron density
or the kinetic-energy density
. Most of them can be classified into one of three main subcategories, depending on the variables on which
depends:
- Local density approximation (LDA):

leading to the exchange-correlation potential

- Generalized-gradient approximation (GGA):


Most of them are either of the generalized-gradient approximation (GGA) or of the meta-GGA.

- the functionals of the generalized-gradient approximation (GGA) and
that, in addition to the electron density
and the gradient
, depend also on
- the kinetic-energy density
, and/or
- the Laplacian of the electron density
.
Thus, the exchange-correlation energy can be written as

which leads to the exchange-correlation potential having the form

Although meta-GGAs are slightly more expensive than GGAs, they are still fast to evaluate and appropriate for very large systems. Furthermore, meta-GGAs can be more accurate than GGAs and more broadly applicable. Note that as in most other codes, meta-GGAs are implemented in VASP (see METAGGA) within the generalized KS scheme[1].
How to
A meta-GGA functional can be used by specifying
in the INCAR file.
How to do a band-structure calculation using meta-GGA functionals.
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