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Record W2900701651 · doi:10.1073/pnas.1814300115

Visualizing atomic sizes and molecular shapes with the classical turning surface of the Kohn–Sham potential

2018· article· en· W2900701651 on OpenAlexafffund
Egor Ospadov, Jianmin Tao, Viktor N. Staroverov, John P. Perdew

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaNational Science Foundation
KeywordsIonic bondingKohn–Sham equationsvan der Waals forceCovalent bondSurface (topology)Chemical physicsAtomic orbitalMoleculeElectronVan der Waals surfaceQuantumChemistryQuantum mechanicsAtomic physicsMolecular physicsVan der Waals radiusPhysicsComputational chemistryDensity functional theoryGeometryMathematicsIon

Abstract

fetched live from OpenAlex

Significance Can quantum mechanics predict a well-defined and chemically intuitive size and shape for an atom or a molecule? We show that the bounding surface of a chemical species can be naturally defined as the classical turning surface of the Kohn–Sham potential—an effective potential that, acting on noninteracting electrons, yields the ground-state density of the real system. The atomic and ionic radii defined in this manner display all expected periodic trends, while the ratio of a bond length to the sum of atomic or ionic radii identifies the type of the bond (covalent, ionic, hydrogen, or van der Waals). The proposed approach permits a visual representation of chemical species that is intuitive and quantum-mechanically rigorous at the same time.

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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.015
GPT teacher head0.286
Teacher spread0.272 · 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

Citations44
Published2018
Admission routes2
Has abstractyes

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