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Record W1658938167 · doi:10.1063/1.3109897

Path-integral Monte Carlo simulation of ν3 vibrational shifts for CO2 in (He)n clusters critically tests the He–CO2 potential energy surface

2009· article· en· W1658938167 on OpenAlexaff
Hui Li, Nicholas Blinov, Pierre–Nicholas Roy, Robert J. Le Roy

Bibliographic record

VenueThe Journal of Chemical Physics · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum, superfluid, helium dynamics
Canadian institutionsUniversity of AlbertaNational Institute for NanotechnologyUniversity of Waterloo
Fundersnot available
KeywordsMonte Carlo methodPath integral Monte CarloStatistical physicsExcited statePath integral formulationRange (aeronautics)PhysicsPotential energy surfaceCluster (spacecraft)Computational physicsAtomic physicsQuantum Monte CarloMathematicsMaterials scienceQuantum mechanicsMoleculeStatistics

Abstract

fetched live from OpenAlex

Path-integral Monte Carlo simulations of the nu(3) vibrational band origin frequency shifts of CO(2) in (He)(n) clusters for n=1-40 show that although only the asymmetric-stretch mode of CO(2) is being excited, the effect of the associated change in the average value of Q(1) cannot be ignored. When this fourth degree of freedom is taken into account, the resulting predicted vibrational frequency shifts are in excellent agreement with experiment across this whole range of cluster size. It is also shown that the quality of predictions obtained from simulations on a given potential energy surface can depend significantly on the choice of the analytic function used to represent it.

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)0.001
metaresearch head score (Gemma)0.003
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: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.250
Teacher spread0.242 · 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

Citations55
Published2009
Admission routes1
Has abstractyes

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