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Record W2021950332 · doi:10.1063/1.481734

Crystallization of dipolar spheres: A discussion of second-order density functional theory

2000· article· en· W2021950332 on OpenAlexaff
Sabine H. L. Klapp, G. N. Patey

Bibliographic record

VenueThe Journal of Chemical Physics · 2000
Typearticle
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFerroelectricityIsotropyDensity functional theoryDipoleSPHERESHard spheresCondensed matter physicsPhase (matter)Statistical physicsPhase transitionPhysicsMaterials scienceThermodynamicsQuantum mechanics

Abstract

fetched live from OpenAlex

We investigate the application of second-order density functional theory to the crystallization of dipolar hard and soft spheres. It is shown that the results are highly sensitive to the repulsive part of the pair potential, and to the symmetry of the fluid phase considered in the density functional theory. In general, the theoretical results for dipolar soft spheres (with r−12 repulsion) are found to be physically more reasonable than those obtained for dipolar hard spheres. In particular, the fluid–solid density gap is greatly reduced and lies in the range expected on physical grounds and from simulations. However, theories constructed by expanding about the isotropic fluid phase overestimate the stability of the ferroelectric solid for both models, and fail to predict the stable ferroelectric fluids found in computer simulations. We have carried out calculations using ferroelectric dipolar fluids with both perfect and fluctuating orientational order as “starting points” in the density functional expansion. The results demonstrate the high sensitivity of the theory to the underlying fluid state, and strongly indicate the existence of stable ferroelectric fluids in accord with computer simulations.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.942

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)0.0010.000

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 head0.006
GPT teacher head0.190
Teacher spread0.184 · 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 designBench or experimental
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

Citations15
Published2000
Admission routes1
Has abstractyes

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