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Record W2896120399 · doi:10.1073/pnas.1808056115

Disorder induced power-law gaps in an insulator–metal Mott transition

2018· article· en· W2896120399 on OpenAlexafffund
Zhenyu Wang, Yoshinori Okada, Jared O’Neal, Wenwen Zhou, Daniel Walkup, Chetan Dhital, Tom Hogan, J. P. Clancy, Young‐June Kim, Yongfeng Hu, Luiz H. Santos, Stephen D. Wilson, Nandini Trivedi, Vidya Madhavan

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Condensed Matter Physics
Canadian institutionsCanadian Light Source (Canada)University of Toronto
FundersDivision of Materials ResearchCanadian Institutes of Health ResearchGordon and Betty Moore Foundation
KeywordsCondensed matter physicsAntiferromagnetismPhysicsSuperconductivityOrder (exchange)Mott insulatorPower lawMathematics

Abstract

fetched live from OpenAlex

Significance Correlated electron systems often show unexpected behavior that defies theoretical explanations. One such mystery is the universal presence of V-shaped gaps with surprisingly linear energy dependence, whose origins are as-yet unknown. Conventional wisdom implicates static order like charge density waves or fluctuations of a nearby order parameter like superconductivity or antiferromagnetism. However, adding dopants to correlated systems inevitably leads to the opposite of order—i.e., electronic disorder—which begs the question: Could disorder create well-defined signatures in electronic properties? By carefully choosing a material with no additional order, we show that order is not the only path to gaps and that disorder may play a surprising role in generating universal signatures in the density of states of disordered correlated systems.

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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.024
GPT teacher head0.304
Teacher spread0.280 · 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

Citations33
Published2018
Admission routes2
Has abstractyes

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