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Record W1974935264 · doi:10.1063/1.1559479

Using C3v symmetry with polyspherical coordinates for methane

2003· article· en· W1974935264 on OpenAlexaff
Xiaogang Wang, Tucker Carrington

Bibliographic record

VenueThe Journal of Chemical Physics · 2003
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHamiltonian (control theory)Hamiltonian matrixSymmetry groupMatrix representationSymmetry operationOperator (biology)Symmetry (geometry)PhysicsComputationLanczos resamplingQuantum mechanicsMathematical physicsGroup (periodic table)MathematicsChemistrySymmetric matrixEigenvalues and eigenvectorsAlgorithmGeometryMathematical optimization

Abstract

fetched live from OpenAlex

It is well known that the group of operators that commutes with the Hamiltonian operator can be used to facilitate the calculation of energy levels. Due to numerical errors in the computation of Hamiltonian matrix elements, it may happen that the matrix representation of a group operator does not commute with the Hamiltonian matrix although the group operator does commute with the Hamiltonian operator. We demonstrate that it is possible, even in this case, to use the single-symmetry and multisymmetry symmetry-adapted Lanczos (SAL) methods to efficiently compute energy levels. The two SAL methods are applied to the calculation of the bend levels of methane using the G6 symmetry group and polyspherical angles. We show that although potential matrix elements are corrupted by quadrature error, it is nonetheless possible to take advantage of the full symmetry of the polyspherical basis. For a CX3Y-type molecule the symmetry-adapted method of this paper would enable one to exploit all of the symmetry of the molecule.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.285
Teacher spread0.262 · 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

Citations25
Published2003
Admission routes1
Has abstractyes

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