MétaCan
Menu
← Back to cohort
Record W4401909433 · doi:10.1103/physrevb.110.085154

Investigating the role of anion polarizability in Fe-based superconductors via light-matter interaction

2024· article· en· W4401909433 on OpenAlexafffund
Xiaoxiao Xiong, Fabio Boschini, Mona Berciu

Bibliographic record

VenuePhysical review. B./Physical review. B · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhysics of Superconductivity and Magnetism
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversity of British Columbia
FundersStewart Blusson Quantum Matter Institute, University of British ColumbiaFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaMinistère du Développement Économique, de l’Innovation et de l’Exportation
KeywordsPolarizabilityIonSuperconductivityChemical physicsMaterials sciencePhysicsChemistryCondensed matter physicsQuantum mechanicsMolecule

Abstract

fetched live from OpenAlex

The polarizability of nearby ions may have a significant impact on electron interactions in solids, but only limited experimental data are available to support this picture. In this work, using a highly simplified description of the prototypical FeAs superconducting layer, we show how external optical excitation of the As $4p\text{\ensuremath{-}}5s$ splitting can lead to a significant modulation of the polarization-mediated effective interactions between carriers. Our results suggest that even perturbative external fields, approximately 2 orders of magnitude smaller than the internal field generated by charge carriers, might enable the exploration of the role of the anion's polarizability in determining the correlated physics, although more detailed modeling is needed to decide the optimal ways to achieve this.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.331
Teacher spread0.317 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same venuePhysical review. B./Physical review. B→Same topicPhysics of Superconductivity and Magnetism→French-language works237,207→