Category:Constrained-random-phase approximation: Difference between revisions

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The '''constrained random-phase approximation''' (CRPA) is a method that allows the calculation of the effective interaction parameter U, J, and J' for model Hamiltonians.  
The '''constrained random-phase approximation''' (CRPA) is a method that allows the calculation of the effective interaction parameter <math>U</math>, <math>J</math>, and <math>J'</math> for model Hamiltonians.  
The main idea is to neglect the screening effects of specific '''target states''' in the screened Coulomb interaction W of the [[The GW approximation of Hedin's equations|GW method]].  
The main idea is to neglect the screening effects of specific target states in the screened Coulomb interaction <math>W</math> of the [[The GW approximation of Hedin's equations|GW method]].  
The resulting partially screened Coulomb interaction is evaluated in a [[Wannier functions|localized basis]] that spans the target space and is described by the model Hamiltonian.  
The resulting partially screened Coulomb interaction is evaluated in a [[Wannier functions|localized basis]] that spans the target space and is described by the model Hamiltonian.  
The target space is usually low-dimensional and therefore allows for the application of a higher-level theory, such as dynamical-mean-field theory (DMFT).
The target space is usually low-dimensional and therefore allows for the application of a higher-level theory, such as dynamical-mean-field theory (DMFT).

Revision as of 12:56, 19 July 2022

The constrained random-phase approximation (CRPA) is a method that allows the calculation of the effective interaction parameter [math]\displaystyle{ U }[/math], [math]\displaystyle{ J }[/math], and [math]\displaystyle{ J' }[/math] for model Hamiltonians. The main idea is to neglect the screening effects of specific target states in the screened Coulomb interaction [math]\displaystyle{ W }[/math] of the GW method. The resulting partially screened Coulomb interaction is evaluated in a localized basis that spans the target space and is described by the model Hamiltonian. The target space is usually low-dimensional and therefore allows for the application of a higher-level theory, such as dynamical-mean-field theory (DMFT).

More information about CRPA is found on the following page:

Constrained–random-phase–approximation_formalism

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