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The formulation of quantum statistical mechanics based on the Feynman path centroid density. I. Equilibrium properties

Cao, Jianshu, Voth, Gregory A.·1994·The Journal of Chemical Physics

Abstract

A formulation of quantum statistical mechanics is discussed in which the Feynman path centroid density in Feynman path integration is recast as the central statistical distribution used to average equilibrium and dynamical quantities. In this formulation, the path integral centroid density occupies the same role as the Boltzmann density in classical statistical mechanics. Therefore, the statistical ensemble of imaginary time path centroid configurations provides the distribution which is used to average the appropriately formulated effective operators and imaginary time correlation functions. An accurate renormalized diagrammatic perturbation theory for the centroid density and centroid-constrained imaginary time propagator will also be described with particular emphasis given to the mathematical advantages arising from the centroid-based formulation. The present paper is concerned with the calculation of equilibrium properties from the centroid perspective, while the companion paper describes a centroid-based formalism for calculating dynamical time correlation functions.

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Metadata

Authors
Cao, J., & Voth, G. A.
Venue
The Journal of Chemical Physics
Published
1994-04-01
Volume
100
Issue
7
Pages
5093-5105
ISSN
  • 0021-9606 (ISSN-L)
  • 1089-7690
OpenAlex
W4247799855
Cited by
365
References
0
FWCI
6.35
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The formulation of quantum statistical mechanics based on the Feynman path centroid density. I. Equilibrium properties. https://app.cochat.ai/papers/10.1063/1.467175
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Cao, J., & Voth, G. A. (1994). The formulation of quantum statistical mechanics based on the Feynman path centroid density. I. Equilibrium properties. The Journal of Chemical Physics. https://doi.org/10.1063/1.467175

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.