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Record W1568368558 · doi:10.1002/9780470749593.hrs018

Using Iterative Methods to Compute Vibrational Spectra

2011· other· en· W1568368558 on OpenAlexaff
Tucker Carrington

Bibliographic record

Venuenot available
Typeother
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsPolyatomic ionEigenvalues and eigenvectorsHamiltonian (control theory)Hamiltonian matrixMatrix (chemical analysis)Iterative methodBasis functionMoleculeBasis setSpectral lineBasis (linear algebra)Contraction (grammar)Computer scienceComputational chemistryAlgorithmPhysicsMathematicsChemistryQuantum mechanicsMathematical optimizationSymmetric matrixGeometry

Abstract

fetched live from OpenAlex

Abstract In this article, some modern methods for computing vibrational energies of polyatomic molecules are reviewed. The emphasis is on iterative methods with which a spectrum is obtained by evaluating matrix–vector products. To use such methods one does not need to compute or store Hamiltonian matrix elements. If each basis function is a product of functions of a single coordinate, matrix–vector products can be computed by doing sequentailly sums associated with indices of the individual coordinates. The corresponding algorithm is simple and useful, but, with current computers, it cannot be applied to compute the spectrum of a six‐atom molecule. For molecules with six or more atoms (and often for molecules with five atoms), it is necessary to use basis set contraction. A contraction method is presented that has been used for methane and CH5+.

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)0.004
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: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.049
GPT teacher head0.365
Teacher spread0.316 · 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
GenreMethods

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

Citations12
Published2011
Admission routes1
Has abstractyes

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