Adaptive cluster approximation for reduced density-matrix functional theory

作者:Schade Robert*; Bloechl Peter E
来源:PHYSICAL REVIEW B, 2018, 97(24): 245131.
DOI:10.1103/PhysRevB.97.245131

摘要

A method, called the adaptive cluster approximation (ACA), for single-impurity Anderson models is proposed. It is based on the 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 exactly 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.

  • 出版日期2018-6-20