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Record W2053792784 · doi:10.1209/0295-5075/93/50004

Quantitative results for square gradient models of fluids

2011· article· en· W2053792784 on OpenAlexaff
Lingti Kong, Dan Vriesinga, Colin Denniston

Bibliographic record

VenueEurophysics Letters (EPL) · 2011
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsWestern University
Fundersnot available
KeywordsSquare (algebra)Statistical physicsDensity gradientFinite potential wellWavelengthElasticity (physics)Molecular dynamicsPhysicsTemperature gradientMeasure (data warehouse)ThermodynamicsChemistryOpticsMathematicsGeometryComputer scienceQuantum mechanics

Abstract

fetched live from OpenAlex

Square gradient models for fluids are extensively used because they are believed to provide a good qualitative understanding of the essential physics. However, unlike elasticity theory for solids, there are few quantitative results for specific (as opposed to generic) fluids. Indeed the only numerical value of the square gradient coefficients for specific fluids have been inferred from attempts to match macroscopic properties such as surface tensions rather than from direct measurement. We employ all-atom molecular dynamics, using the TIP3P and OPLS force fields, to directly measure the coefficients of the density gradient expansion for several real fluids. For all liquids measured, including water, we find that the square gradient coefficient is negative, suggesting the need for some regularization of a model including only the square gradient, but only at wavelengths comparable to the molecular separation of molecules. The implications for liquid-gas interfaces are also examined. Remarkably, the square gradient model is found to give a reasonably accurate description of density fluctuations in the liquid state down to wavelengths close to atomic size.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.223
Teacher spread0.183 · 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 teacher head, 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

Citations4
Published2011
Admission routes1
Has abstractyes

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