MétaCan
Menu
Back to cohort
Record W2067544869 · doi:10.1063/1.2730837

Polarizable and flexible model for ethanol

2007· article· en· W2067544869 on OpenAlexaff
Shihao Wang, N. M. Cann

Bibliographic record

VenueThe Journal of Chemical Physics · 2007
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsQueen's University
Fundersnot available
KeywordsEthanolComputer scienceChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

A polarizable, flexible model for ethanol is obtained based on an extensive series of B3LYP/6-311++G(d,p) calculations and molecular dynamics simulations. The ethanol model includes electric-field dependence in both the atomic charges and the intramolecular degrees of freedom. Field-dependent intramolecular potentials have been attempted only once previously, for OH and HH stretches in water [P. Cicu et al., J. Chem. Phys. 112, 8267 (2000)]. The torsional potential involving the hydrogen-bonding hydrogen in ethanol is found to be particularly field sensitive. The methodology for developing field-dependent potentials can be readily generalized to other molecules and is discussed in detail. Molecular dynamics simulations of bulk ethanol are performed and the results are assessed based on comparisons with the self-diffusion coefficient [N. Karger et al., J. Chem. Phys. 93, 3437 (1990)], dielectric constant [J. T. Kindt and C. A. Schmuttenmaer, J. Phys. Chem. 100, 10373 (1996)], enthalpy of vaporization [R. C. Wilhoit and B. J. Zwolinski, J. Phys. Chem. Ref. Data, Suppl. 2, 2 (1973)], and experimental interatomic distributions [C. J. Benmore and Y. L. Loh, J. Chem. Phys. 112, 5877 (2000)]. The simultaneous variation of the atomic charges and the intramolecular potentials requires modified equations of motion and a multiple time step algorithm has been implemented to solve these equations. The article concludes with a discussion of the bulk structure and properties with an emphasis on the hydrogen bonding network.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score0.196

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 head0.015
GPT teacher head0.243
Teacher spread0.227 · 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

Citations19
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Chemical PhysicsSame topicPhase Equilibria and ThermodynamicsFrench-language works237,207