Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 306

The DOI matches its Crossref record; 110 of the 110 references it cites were found in a registry or an index.

Computational statistical mechanics methodology, applications and supercomputing

Abraham, Farid F.·1986·Advances in Physics

Abstract

Computer simulation is adding a new dimension to scientific investigation, establishing a role of equal importance with the traditional approaches of experiment and theory. In this paper, we provide a text for understanding the computer simulation methodology of classical statistical mechanics. After developing the theoretical basis of the simulation techniques, the Monte Carlo and Langevin methods and various molecular dynamics methods are described. A very limited discussion is provided on interatomic potential functions, numerical integration schemes, and general simulation procedures for modelling different physical situations and for circumventing excessive computational burdens. The simulation methods are then illustrated using a variety of physical problems studied over the last several years at our laboratory. They include spinodal decomposition of a two-dimensional (2D) fluid, the melting of 2D and quasi-2D films, the structure and energetics of an incommensurate physisorbed film, and the roughening of a silicon solid-melt interface. Finally, we discuss the supercomputer issue, both in terms of super problems for supercomputers and parallel architectures leading to near-future computers that will be one thousand times faster than those currently available.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/00018738600101851

Cites

110 of 110 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Cites

Show all 110 references

Cited by

See all 306

Metadata

Authors
Abraham, F. F.
Venue
Advances in Physics
Published
1986-01
Volume
35
Issue
1
Pages
1-111
ISSN
  • 0001-8732 (ISSN-L)
  • 1460-6976
OpenAlex
W2002118881
Cited by
306
References
110
FWCI
20.03
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: Computational statistical mechanics methodology, applications and supercomputing. https://app.cochat.ai/papers/10.1080/00018738600101851
The paper
Abraham, F. F. (1986). Computational statistical mechanics methodology, applications and supercomputing. Advances in Physics. https://doi.org/10.1080/00018738600101851

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.