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Record W2008603380 · doi:10.1063/1.4790607

Density functional theory and global optimization study of Sn<i>m</i>Pb<i>n</i> clusters (7 ⩽ <i>m</i> + <i>n</i> ⩽ 12, 0 ⩽ <i>m</i>/(<i>m</i> + <i>n</i>) ⩽ 1)

2013· article· en· W2008603380 on OpenAlexaff
Slava Orel, René Fournier

Bibliographic record

VenueThe Journal of Chemical Physics · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsYork University
Fundersnot available
KeywordsDipoleChemistryAtom (system on chip)Cluster (spacecraft)Basis setDensity functional theoryAtomic physicsStoichiometryBinary numberMolecular physicsPhysicsComputational chemistryPhysical chemistryMathematics

Abstract

fetched live from OpenAlex

The global minima of the neutral binary Sn(m)Pb(n) atomic clusters, 7 ≤ m + n ≤ 12, of all the possible stoichiometric ratios have been found using tabu search in descriptor space and density functional theory. The effects of spin-orbit coupling on optimized geometries and energies are important. All of the binary clusters form substitution alloys. Apart from the 11-atom case, the pure clusters of the same size have the same ground state geometry. The relative energies of the isomers of a cluster depend on, in order of decreasing importance: the overall geometry; the specific sites occupied by the two atom types; and the degree of segregation. The total cohesive energy difference between the lowest energy homotops is typically on the order of 0.02 eV. The mixing/segregation trends are found to be very different depending on the size of the basis set. Calculations generally overestimate the dipole moments. The trends in calculated dipole moments agree with experiment for the lead-rich clusters, and to a lesser degree, the tin-rich clusters.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.222
Teacher spread0.213 · 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

Citations7
Published2013
Admission routes1
Has abstractyes

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