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Record W2078673875 · doi:10.1103/physreve.70.011201

Modeling inhomogeneous van der Waals fluids using an analytical direct correlation function

2004· article· en· W2078673875 on OpenAlexaff
Yiping Tang, Jianzhong Wu

Bibliographic record

VenuePhysical Review E · 2004
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsHoneywell (Canada)
Fundersnot available
Keywordsvan der Waals forceHelmholtz free energyRadial distribution functionLimit (mathematics)Statistical physicsMean field theoryPhysicsCorrelation function (quantum field theory)Hard spheresDistribution functionComplex fluidField (mathematics)ThermodynamicsMolecular dynamicsMathematicsQuantum mechanicsMathematical analysis

Abstract

fetched live from OpenAlex

Rosenfeld's perturbative method [J. Chem. Phys. 98, 8126 (1993)]] for constructing the Helmholtz energy functional of classical systems is applied to studying inhomogeneous Lennard-Jones fluids, in which the key input-the bulk direct correlation function-is obtained from the first-order mean-spherical approximation (FMSA) [J. Chem. Phys. 118, 4140 (2003)]]. Preserving its high fidelity at the bulk limit, the FMSA shows stable and satisfactory performance for a variety of inhomogeneous Lennard-Jones fluids including those near hard walls, inside slit pores, and around colloidal particles. In addition, the inhomogeneous FMSA reproduces reliably the radial distribution function at its bulk limit. The FMSA is found, in particular, much better than the mean-field theory for fluids near hard surfaces. Unlike alternative non-mean-field approaches, the FMSA is computationally as efficient as the mean-field theory, free of any numerical determination of structure information, weight functions, or empirical parameters.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.295
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations122
Published2004
Admission routes1
Has abstractyes

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