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Record W2135739713 · doi:10.1002/qua.10781

Optimization of numerical orbitals in molecular MO‐LCAO calculations

2003· article· en· W2135739713 on OpenAlexaff
James D. Talman

Bibliographic record

VenueInternational Journal of Quantum Chemistry · 2003
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsLinear combination of atomic orbitalsSTO-nG basis setsSlater-type orbitalBasis setAtomic orbitalMolecular orbitalMolecular orbital theoryCubic harmonicQuantum mechanicsLocalized molecular orbitalsValence bond theoryGaussianPhysicsNatural bond orbitalChemistryAtomic physicsDensity functional theoryMolecule

Abstract

fetched live from OpenAlex

Abstract The problem of the numerical determination of the atomic orbitals used in the construction of molecular orbitals in MO‐LCAO calculations is studied. This gives rise to a two‐fold optimization problem; the Roothaan–Hall Hartree–Fock problem of minimizing the energy with respect to the molecular orbital expansion coefficients and the variational problem of optimizing the atomic orbitals. The variational equations for the atomic orbitals are derived and the methods of solution described. The methods of computing the required multicenter integrals for numerical orbitals using Fourier transform methods are also reviewed. The calculation of energy gradients within this framework is discussed. Results are presented for a number of small molecules. Possible advantages of this approach are smaller basis sets are required and the wave functions at the nuclei can be much better approximated than with Gaussian‐type orbitals. As well, the fact that orbitals are better approximated implies that basis set superposition errors for dissociation energies calculated in the Hartree–Fock approximation will be reduced. © 2003 Wiley Periodicals, Inc. Int J Quantum Chem, 2003

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 score0.677
Threshold uncertainty score0.370

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 head0.008
GPT teacher head0.276
Teacher spread0.268 · 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

Citations16
Published2003
Admission routes1
Has abstractyes

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