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Record W2319620309 · doi:10.1021/ct4004146

Removal of Basis-Set Artifacts in Kohn–Sham Potentials Recovered from Electron Densities

2013· article· en· W2319620309 on OpenAlexafffund
Alex P. Gaiduk, Ilya G. Ryabinkin, Viktor N. Staroverov

Bibliographic record

VenueJournal of Chemical Theory and Computation · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsKohn–Sham equationsBasis setSet (abstract data type)ElectronBasis (linear algebra)PhysicsComputer scienceDensity functional theoryChemistryMaterials scienceComputational chemistryMathematicsQuantum mechanicsGeometry

Abstract

fetched live from OpenAlex

Kohn-Sham effective potentials recovered from Gaussian-basis-set electron densities exhibit large oscillations and asymptotic divergences not found in exact potentials and in functional derivatives of approximate density functionals. We show that the detailed structure of these oscillations and divergences is almost exclusively determined by the basis set in terms of which the reference density is expressed, and is almost independent of the density-functional or wave function method used for computing the density. Based on this observation, we propose a smoothening scheme in which most basis-set artifacts in a Kohn-Sham potential recovered from a given density are removed by subtracting the oscillation profile of the exchange-only local-density approximation potential computed in the same basis set as the reference density. The correction allows one to obtain smooth Kohn-Sham potentials from electron densities even for small Gaussian basis sets and greatly reduces discrepancies between the original (input) density and the density obtained from the reconstructed potential.

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 score0.627
Threshold uncertainty score0.329

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 head0.009
GPT teacher head0.250
Teacher spread0.241 · 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

Citations47
Published2013
Admission routes2
Has abstractyes

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