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arXiv · 1612.06692

Adaptive cluster approximation for reduced density-matrix functional theory

Robert Schade·Peter E. Blöchl
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Abstract

A method, called the adaptive cluster approximation (ACA), for single-impurity Anderson models is proposed. It is based on reduced density-matrix functional theory, where the one-particle reduced density matrix is used as the basic variable. The adaptive cluster approximation introduces a unitary transformation of the bath states such that the effect of the bath is concentrated to a small cluster around the impurity. For this small effective system one can then either calculate the reduced density-matrix functional numerically exact from Levy's constrained-search formalism or approximate it by an implicit approximation of the reduced density-matrix functional. The method is evaluated for single-impurity Anderson models with finite baths. The method converges rapidly to the exact result with the size of the effective bath.

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Robert Schade, Peter E. Blöchl. 2018-05-16. Adaptive cluster approximation for reduced density-matrix functional theory. https://doi.org/10.1103/physrevb.97.245131

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