The DOI matches its Crossref record.
Correlation Energy of an Electron Gas at High Density
Abstract
The correlation energy of an electron gas at high density is evaluated up to terms of orders ${({r}_{s})}^{0}$ and $log{r}_{s}$. It is shown that the correlation energy to this order can be evaluated without using perturbation treatment. The result obtained coincides with the result of formal summation of apparently divergent series arising from small momentum transfer processes which has been discussed fully by Gell-Mann and Brueckner. The method to treat the small momentum transfer effect exactly is given by following the analogy of processes with well-known treatment of systems with Hamiltonians which are bilinear in creation and annihilation operators such as the neutral scalar pair theory. Some simple interpretations of the correlation energy to this order are also given.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Theory of Many-Particle Systems. I
Martin, P. C., & Schwinger, J. (1959) · Physical Review · doi:10.1103/physrev.115.1342 · Cited by 1,816
Random-Phase Approximation in the Theory of Superconductivity
Anderson, P. W. (1958) · Physical Review · doi:10.1103/physrev.112.1900 · Cited by 1,381
Random-phase approximation and its applications in computational chemistry and materials science
Ren, X., Rinke, P., Joas, C., & Scheffler, M. (2012) · Journal of Materials Science · doi:10.1007/s10853-012-6570-4 · Cited by 610
Irreversible Processes in Ionized Gases
Balescu, R. (1960) · The Physics of Fluids · doi:10.1063/1.1706002 · Cited by 555
Electron Self-Energy Approach to Correlation in a Degenerate Electron Gas
Quinn, J. J., & Ferrell, R. A. (1958) · Physical Review · doi:10.1103/physrev.112.812 · Cited by 417
Metadata
- Authors
- Sawada, K.
- Venue
- Physical Review
- Publisher
- American Physical Society (APS)
- Published
- 1957-04-15
- Volume
- 106
- Issue
- 2
- Pages
- 372-383
- ISSN
- 0031-899X (ISSN-L)
- OpenAlex
- W1998527569
- Cited by
- 356
- References
- 0
- FWCI
- 18.11
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Correlation Energy of an Electron Gas at High Density. https://app.cochat.ai/papers/10.1103/physrev.106.372
- The paper
- Sawada, K. (1957). Correlation Energy of an Electron Gas at High Density. Physical Review. https://doi.org/10.1103/physrev.106.372
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.