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Record W2484932966 · doi:10.1103/physrevb.94.195127

Bond disproportionation and dynamical charge fluctuations in the perovskite rare-earth nickelates

2016· article· en· W2484932966 on OpenAlexafffund
R. J. Green, M. W. Haverkort, G. A. Sawatzky

Bibliographic record

VenuePhysical review. B./Physical review. B · 2016
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
KeywordsDisproportionationPerovskite (structure)Rare earthCharge (physics)Earth (classical element)BondMaterials scienceCondensed matter physicsChemical physicsPhysicsAstrobiologyEarth scienceChemistryCrystallographyGeologyBusinessQuantum mechanicsMathematical physicsCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

We present a theory describing the local electronic properties of the perovskite rare-earth nickelates---materials which have negative charge transfer energies, strong O $2p$ -- Ni $3d$ covalence, and breathing-mode lattice distortions at the origin of highly studied metal-insulator and antiferromagnetic ordering transitions. Utilizing a full-orbital, full-correlation double-cluster approach, we find strong charge fluctuations, in agreement with a bond disproportionation interpretation. The double-cluster formulation permits the inclusion of necessary orbital degeneracies and Coulomb interactions to calculate resonant x-ray spectral responses, with which we find excellent agreement with well-established experimental results. This previously absent, crucial link between theory and experiment provides validation of the recently proposed bond disproportionation theory, and provides an analysis methodology for spectroscopic studies of engineered phases of nickelates and other high-valence transition-metal compounds.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.001

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 head0.010
GPT teacher head0.299
Teacher spread0.289 · 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.

Study designTheoretical or conceptual
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

Citations149
Published2016
Admission routes2
Has abstractyes

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