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Record W2165928548 · doi:10.1103/physrevb.91.121114

Hybridization effects and bond disproportionation in the bismuth perovskites

2015· article· en· W2165928548 on OpenAlexafffund
Kateryna Foyevtsova, Arash Khazraie, Ilya Elfimov, G. A. Sawatzky

Bibliographic record

VenuePhysical Review B · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic and transport properties of perovskites and related materials
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced Research
KeywordsDisproportionationBismuthIonic bondingAtomic orbitalOctahedronOrbital hybridisationCrystallographyPerovskite (structure)Condensed matter physicsMaterials scienceChemistryElectronPhysicsCrystal structureIonQuantum mechanicsMolecular orbital theory

Abstract

fetched live from OpenAlex

We propose a microscopic description of the bond-disproportionated insulating state in the bismuth perovskites $X{\mathrm{BiO}}_{3}$ ($X=\text{Ba},\text{Sr}$) that recognizes the bismuth-oxygen hybridization as a dominant energy scale. It is demonstrated by using electronic structure methods that the breathing distortion is accompanied by spatial condensation of hole pairs into local, molecularlike orbitals of the ${A}_{1g}$ symmetry composed of O-$2{p}_{\ensuremath{\sigma}}$ and Bi-$6s$ atomic orbitals of collapsed ${\mathrm{BiO}}_{6}$ octahedra. The primary importance of oxygen $p$ states is thus revealed, in contrast to a popular picture of a purely ionic ${\mathrm{Bi}}^{3+}/{\mathrm{Bi}}^{5+}$ charge disproportionation. Octahedra tilting is shown to enhance the breathing instability by means of a nonuniform band narrowing. We argue that the formation of localized states upon breathing distortion is, to a large extent, a property of the oxygen sublattice, and we expect similar hybridization effects in other perovskites involving formally high oxidation state cations.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.365
Threshold uncertainty score0.190

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 head0.014
GPT teacher head0.268
Teacher spread0.253 · 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

Citations83
Published2015
Admission routes2
Has abstractyes

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