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Record W2750329936 · doi:10.1063/1.4985874

Orbital angular momentum eigenfunctions for fast and numerically stable evaluations of closed-form pseudopotential matrix elements

2017· article· en· W2750329936 on OpenAlexaff
Anguang Hu, Nora W. C. Chan, Brett I. Dunlap

Bibliographic record

VenueThe Journal of Chemical Physics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Molecular Physics
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsAngular momentumPseudopotentialPhysicsTotal angular momentum quantum numberAzimuthal quantum numberGaussianAngular momentum couplingEigenvalues and eigenvectorsQuantum mechanicsEigenfunctionMathematical analysisMathematics

Abstract

fetched live from OpenAlex

The computation of s-type Gaussian pseudopotential matrix elements involving low powers of the distance from the pseudopotential center using Gaussian orbitals can be reduced to familiar integrals. They may be directly expressed as either simple three-center overlap integrals for even powers of the radial distance from the pseudopotential center or related to the three-center nuclear integrals of a Gaussian charge distribution for odd powers. Orbital angular momentum about each atom is added to these integrals by solid-harmonic differentiation with respect to its center. The solid-harmonic addition theorem allows all the integrals to be factored into products of invariant one-dimensional integrals involving the Gaussian exponents and angular factors that contain the azimuthal quantum numbers but are independent of all Gaussian exponents. Precomputing the angular factors allow looping over all Gaussian exponents about the three centers. The fact that solid harmonics are eigenstates of angular momentum removes the singularities seen in previous treatments of pseudopotential matrix elements.

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)0.008
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 score0.021
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0210.008

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 head0.013
GPT teacher head0.298
Teacher spread0.285 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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