The DOI matches its Crossref record; 184 of the 184 references it cites were found in a registry or an index.
On the inner workings of Monte Carlo codes
Open accessAbstract
We review state-of-the-art Monte Carlo (MC) techniques for computing fluid coexistence properties (Gibbs simulations) and adsorption simulations in nanoporous materials such as zeolites and metal–organic frameworks. Conventional MC is discussed and compared to advanced techniques such as reactive MC, configurational-bias Monte Carlo and continuous fractional MC. The latter technique overcomes the problem of low insertion probabilities in open systems. Other modern methods are (hyper-)parallel tempering, Wang–Landau sampling and nested sampling. Details on the techniques and acceptance rules as well as to what systems these techniques can be applied are provided. We highlight consistency tests to help validate and debug MC codes.
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
Fast Parallel Algorithms for Short-Range Molecular Dynamics
Plimpton, S. (1995) · Journal of Computational Physics · doi:10.1006/jcph.1995.1039 · Cited by 44,721
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Computer Simulation of Liquids
Allen, M. P., & Tildesley, D. J. (2017) · doi:10.1093/oso/9780198803195.001.0001 · Cited by 20,945
Jorgensen, W. L., Maxwell, D. S., & Tirado-Rives, J. (1996) · Journal of the American Chemical Society · doi:10.1021/ja9621760 · Cited by 15,409
All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins
MacKerell, A. D., Bashford, D., Bellott, M., Dunbrack, R. L., Evanseck, J. D., Field, M. J., Fischer, S., Gao, J., Guo, H., Ha, S., et al. (1998) · The Journal of Physical Chemistry B · doi:10.1021/jp973084f · Cited by 14,311
A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules
Cornell, W. D., Cieplak, P., Bayly, C. I., Gould, I. R., Merz, K. M., Ferguson, D. M., Spellmeyer, D. C., Fox, T., Caldwell, J. W., & Kollman, P. A. (1995) · Journal of the American Chemical Society · doi:10.1021/ja00124a002 · Cited by 13,103
UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations
Rappe, A. K., Casewit, C. J., Colwell, K. S., Goddard, W. A., & Skiff, W. M. (1992) · Journal of the American Chemical Society · doi:10.1021/ja00051a040 · Cited by 10,557
GROMACS: A message-passing parallel molecular dynamics implementation
Berendsen, H. J. C., van der Spoel, D., & van Drunen, R. (1995) · Computer Physics Communications · doi:10.1016/0010-4655(95)00042-e · Cited by 10,405
Hansen, J. P., McDonald, I. R., & Henderson, D. J. (1976) · Cited by 7,860
GROMACS 3.0: a package for molecular simulation and trajectory analysis
Lindahl, E., Hess, B., & van der Spoel, D. (2001) · Journal of Molecular Modeling · doi:10.1007/s008940100045 · Cited by 6,977
Cited by
Water Stability and Adsorption in Metal–Organic Frameworks
Burtch, N. C., Jasuja, H., & Walton, K. S. (2014) · Chemical Reviews · doi:10.1021/cr5002589 · Cited by 2,730
Self-citationSeparation of rare gases and chiral molecules by selective binding in porous organic cages
Chen, L., Reiss, P. S., Chong, S. Y., Holden, D., Jelfs, K. E., Hasell, T., Little, M. A., Kewley, A., Briggs, M. E., Stephenson, A., et al. (2014) · Nature Materials · doi:10.1038/nmat4035 · Cited by 666
Advances in theory and their application within the field of zeolite chemistry
Van Speybroeck, V., Hemelsoet, K., Joos, L., Waroquier, M., Bell, R. G., & Catlow, C. R. A. (2015) · Chemical Society Reviews · doi:10.1039/c5cs00029g · Cited by 516
Chung, Y. G., Gómez-Gualdrón, D. A., Li, P., Leperi, K. T., Deria, P., Zhang, H., Vermeulen, N. A., Stoddart, J. F., You, F., Hupp, J. T., et al. (2016) · Science Advances · doi:10.1126/sciadv.1600909 · Cited by 321
Modeling of multi-scale transport phenomena in shale gas production — A critical review
Wang, H., Chen, L., Qu, Z., Yin, Y., Kang, Q., Yu, B., & Tao, W.-Q. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.114575 · Cited by 293
Metadata
- Authors
- Dubbeldam, D., Torres-Knoop, A., & Walton, K. S.
- Venue
- Molecular Simulation
- Publisher
- Informa UK Limited
- Published
- 2013-12
- Volume
- 39
- Issue
- 14-15
- Pages
- 1253-1292
- ISSN
- 0892-7022 (ISSN-L)
- 1029-0435
- OpenAlex
- W2149529230
- Cited by
- 422
- References
- 184
- FWCI
- 14.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
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: On the inner workings of Monte Carlo codes. https://app.cochat.ai/papers/10.1080/08927022.2013.819102
- The paper
- Dubbeldam, D., Torres-Knoop, A., & Walton, K. S. (2013). On the inner workings of Monte Carlo codes. Molecular Simulation. https://doi.org/10.1080/08927022.2013.819102
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.