Category:Meta-GGA: Difference between revisions
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= | '''Meta-GGAs''' are a family of [[exchange-correlation functionals]] that in addition to the electron density <math>n</math>, its gradient <math>\nabla n</math> depend on | ||
* the kinetic-energy density <math>\tau</math>, and/or | |||
* the Laplacian of the electron density <math>\nabla^{2}n</math>. | |||
Thus, the exchange-correlation energy can be written as | |||
:<math>E_{\mathrm{xc}}^{\mathrm{meta-GGA}}=\int\epsilon_{\mathrm{xc}}^{\mathrm{meta-GGA}}(n,\nabla n,\nabla^{2}n,\tau)d^{3}r.</math> | |||
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 {{TAG|METAGGA}}) within the generalized KS scheme{{cite|yang:prb:2016|}}. | |||
== How to == | |||
A '''meta-GGA functional''' can be used by specifying | |||
* the {{TAG|METAGGA}} tag, or | |||
* {{TAG|XC}} tag | |||
in the {{FILE|INCAR}} file. | |||
How to do a [[band-structure calculation using meta-GGA functionals]]. | |||
---- | ---- | ||
[[Category:VASP|PAW]][[Category: | [[Category:VASP|PAW]][[Category:Exchange-correlation functionals]] |
Latest revision as of 06:16, 12 June 2024
Meta-GGAs are a family of exchange-correlation functionals that in addition to the electron density , its gradient depend on
- the kinetic-energy density , and/or
- the Laplacian of the electron density .
Thus, the exchange-correlation energy can be written as
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.