The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
The relative entropy is fundamental to multiscale and inverse thermodynamic problems
Abstract
We show that the relative entropy, Srel≡∑pT ln(pT/pM), provides a fundamental and unifying framework for multiscale analysis and for inverse molecular-thermodynamic problems involving optimization of a model system (M) to reproduce the properties of a target one (T). We demonstrate that the relative entropy serves as a generating function for principles in variational mean-field theory and uniqueness and gives intuitive results for simple case scenarios in model development. Moreover, we suggest that the relative entropy provides a rigorous framework for multiscale simulations and offers new numerical techniques for linking models at different scales. Finally, we show that Srel carries physical significance by using it to quantify the deviations of a three-site model of water from simple liquids, finding that the relative entropy, a thermodynamic concept, even predicts water’s kinetic anomalies.
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
The missing term in effective pair potentials
Berendsen, H. J. C., Grigera, J. R., & Straatsma, T. P. (1987) · The Journal of Physical Chemistry · doi:10.1021/j100308a038 · Cited by 13,345
Reverse Monte Carlo Simulation: A New Technique for the Determination of Disordered Structures
McGreevy, R. L., & Pusztai, L. (1988) · Molecular Simulation · doi:10.1080/08927028808080958 · Cited by 1,678
Relationship between structural order and the anomalies of liquid water
Errington, J. R., & Debenedetti, P. G. (2001) · Nature · doi:10.1038/35053024 · Cited by 1,607
Interatomic Potentials from First-Principles Calculations: The Force-Matching Method
Ercolessi, F., & Adams, J. B. (1994) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/26/8/005 · Cited by 1,455
A Multiscale Coarse-Graining Method for Biomolecular Systems
Izvekov, S., & Voth, G. A. (2005) · The Journal of Physical Chemistry B · doi:10.1021/jp044629q · Cited by 1,197
Interatomic Potentials from First-Principles Calculations – The Force-Matching MethodPreprint
Ercolessi, F., & Adams, J. B. (1994) · Cited by 1,160
Free Energy Calculations: Theory and Applications in Chemistry and Biology
(2007) · Springer Series in CHEMICAL PHYSICS · doi:10.1007/978-3-540-38448-9 · Cited by 894
Lyubartsev, A. P., & Laaksonen, A. (1995) · Physical Review E · doi:10.1103/physreve.52.3730 · Cited by 852
Relation between the transport coefficients and the internal entropy of simple systems
Rosenfeld, Y. (1977) · Physical Review A · doi:10.1103/physreva.15.2545 · Cited by 688
Multiscale coarse graining of liquid-state systems
Izvekov, S., & Voth, G. A. (2005) · The Journal of Chemical Physics · doi:10.1063/1.2038787 · Cited by 653
Cited by
Computer Simulation of Liquids
Allen, M. P., & Tildesley, D. J. (2017) · doi:10.1093/oso/9780198803195.001.0001 · Cited by 20,945
Perspective: Coarse-grained models for biomolecular systems
Noid, W. G. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4818908 · Cited by 893
How Water’s Properties Are Encoded in Its Molecular Structure and Energies
Brini, E., Fennell, C. J., Fernandez-Serra, M., Hribar-Lee, B., Lukšič, M., & Dill, K. A. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.7b00259 · Cited by 518
Challenges in Multiscale Modeling of Polymer Dynamics
Li, Y., Abberton, B., Kröger, M., & Liu, W. (2013) · Polymers · doi:10.3390/polym5020751 · Cited by 222
“The structure-based methods include the iterative Boltzmann inversion (IBI) method [82,83], the Kirkwood-Buff IBI method [164], the inverse Monte Carlo (IMC) method [165], the relative entropy method [166] and the generalized Yvon–Born–Green theory [167].”
MethodAdvances in coarse-grained modeling of macromolecular complexes
Pak, A. J., & Voth, G. A. (2018) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2018.11.005 · Cited by 145
Metadata
- Authors
- Shell, M. S.
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2008-10-10
- Volume
- 129
- Issue
- 14
- Pages
- 144108-144108
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W2060174126
- Cited by
- 656
- References
- 17
- FWCI
- 6.09
- 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: The relative entropy is fundamental to multiscale and inverse thermodynamic problems. https://app.cochat.ai/papers/10.1063/1.2992060
- The paper
- Shell, M. S. (2008). The relative entropy is fundamental to multiscale and inverse thermodynamic problems. The Journal of Chemical Physics. https://doi.org/10.1063/1.2992060
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.