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Record W2027356989 · doi:10.1002/wcms.73

Computing ro‐vibrational spectra of van der Waals molecules

2011· article· en· W2027356989 on OpenAlexaff
Tucker Carrington, Xiaogang Wang

Bibliographic record

VenueWiley Interdisciplinary Reviews Computational Molecular Science · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsQueen's University
Fundersnot available
Keywordsvan der Waals forceIntramolecular forceVan der Waals surfaceMoleculeSpectral lineChemistryKinetic energyVan der Waals strainComputational chemistryBasis (linear algebra)PhysicsVan der Waals radiusQuantum mechanicsMathematicsGeometry

Abstract

fetched live from OpenAlex

Abstract This article reviews methods for computing ro‐vibrational spectra of van der Waals molecules. Due to the presence of large‐amplitude motion, calculations often play an important role in assigning and understanding the spectra of van der Waals molecules. Fortunately, it is possible to make usefully accurate calculations because important parts of the spectrum can be understood by doing calculations that omit the intramolecular coordinates. In this article, we present new ideas for deriving kinetic energy operators and discuss choosing basis functions and doing the matrix–vector products that are required to obtain a spectrum using the Lanczos algorithm. © 2011 John Wiley & Sons, Ltd. WIREs Comput Mol Sci 2011 1 952–963 DOI: 10.1002/wcms.73 This article is categorized under: Theoretical and Physical Chemistry > Spectroscopy

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.026
GPT teacher head0.312
Teacher spread0.287 · 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

Citations39
Published2011
Admission routes1
Has abstractyes

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