The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Generalization of the Wang-Landau method for off-lattice simulations
Open accessAbstract
We present a rigorous derivation for off-lattice implementations of the so-called "random-walk" algorithm recently introduced by Wang and Landau [Phys. Rev. Lett. 86, 2050 (2001)]. Originally developed for discrete systems, the algorithm samples configurations according to their inverse density of states using Monte Carlo moves; the estimate for the density of states is refined at each simulation step and is ultimately used to calculate thermodynamic properties. We present an implementation for atomic systems based on a rigorous separation of kinetic and configurational contributions to the density of states. By constructing a "uniform" ensemble for configurational degrees of freedom-in which all potential energies, volumes, and numbers of particles are equally probable-we establish a framework for the correct implementation of simulation acceptance criteria and calculation of thermodynamic averages in the continuum case. To demonstrate the generality of our approach, we perform sample calculations for the Lennard-Jones fluid using two implementation variants and in both cases find good agreement with established literature values for the vapor-liquid coexistence locus.
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
Cites
Computer Simulation of Liquids
Allen, M. P., & Tildesley, D. J. (2017) · doi:10.1093/oso/9780198803195.001.0001 · Cited by 20,945
Cited by
Adaptive biasing force method for scalar and vector free energy calculations
Darve, E., Rodríguez-Gómez, D., & Pohorille, A. (2008) · The Journal of Chemical Physics · doi:10.1063/1.2829861 · Cited by 871
Metadata
- Authors
- Shell, M. S., Debenedetti, P. G., & Panagiotopoulos, A. Z.
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2002-11-22
- Volume
- 66
- Issue
- 5
- Pages
- 056703-056703
- ISSN
- 1063-651X (ISSN-L)
- 1095-3787
- OpenAlex
- W2126812663
- Cited by
- 227
- References
- 1
- FWCI
- 6.45
- Type
- journal article
- Language
- en
- Open access
- green
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: Generalization of the Wang-Landau method for off-lattice simulations. https://app.cochat.ai/papers/10.1103/physreve.66.056703
- The paper
- Shell, M. S., Debenedetti, P. G., & Panagiotopoulos, A. Z. (2002). Generalization of the Wang-Landau method for off-lattice simulations. Physical Review E. https://doi.org/10.1103/physreve.66.056703
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.