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Record W7117131450 · doi:10.1063/5.0304583

Representation of electron–nucleus cusps in Slater and Gaussian basis sets

2025· article· en· W7117131450 on OpenAlexafffund
Conrad C. Moore, Viktor N. Staroverov

Bibliographic record

VenueThe Journal of Chemical Physics · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsClassification of discontinuitiesWave functionBasis functionSTO-nG basis setsCusp (singularity)Basis (linear algebra)Atomic orbitalGaussianKinetic energy

Abstract

fetched live from OpenAlex

Prior to spherical averaging, the Coulomb cusp of exact electron densities is described by an equation involving two parameters: the nuclear charge Z and a vector quantifying the cusp anisotropy due to the presence of other nuclei. A similar three-parameter equation describes jump discontinuities of exact kinetic energy densities at nuclear positions. We demonstrate that these same equations, with basis-set-dependent parameters, also describe approximate electron and kinetic energy densities expanded in Slater-type orbitals (STOs) and even cuspless Gaussian-type orbitals (GTOs). Apart from the effective Z values, which are zero for GTOs, the cusp-shape parameters derived from STO and GTO wavefunctions are comparable in magnitude and converge to common complete-basis-set limits. These findings provide a rigorous justification for extending the concepts of effective nuclear charge and density anisotropy vectors to approximate wavefunctions within STO and GTO basis sets. Such extensions are not entailed by the properties of exact solutions to the Schrödinger equation and are not automatically applicable to all basis sets.

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)0.002
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0020.001
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 head0.009
GPT teacher head0.282
Teacher spread0.274 · 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 designTheoretical or conceptual
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

Citations1
Published2025
Admission routes2
Has abstractyes

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