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Adaptive cluster approximation for reduced density-matrix functional theory
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DOI：10.1103/PhysRevB.97.245131.png)
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摘要
En 中文
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.
Keyword:
QUANTUM RENORMALIZATION-GROUPS
NATURAL SPIN-ORBITALS
ENERGY FUNCTIONALS
MOLECULAR-DYNAMICS
SYSTEMS
ALGORITHMS
STATES
MODEL
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期刊
IF:
3.7
论文数:
15.4W
被引数:
41.0W
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