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Record W4385017472 · doi:10.48550/arxiv.2307.10389

Magnetization amplification in the interlayer pairing superconductor 4Hb-TaS$_2$

2023· preprint· en· W4385017472 on OpenAlexaff
Chunxiao Liu, Shubhayu Chatterjee, Thomas Scaffidi, Erez Berg, Ehud Altman

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldMaterials Science
TopicIron-based superconductors research
Canadian institutionsUniversity of Toronto
FundersArmy Research OfficeMultidisciplinary University Research InitiativeAspen Center for PhysicsGordon and Betty Moore FoundationNational Science Foundation
KeywordsCondensed matter physicsMagnetizationSuperconductivityPairingPhysicsFermi surfaceSymmetry (geometry)Spin (aerodynamics)Fermi levelQuantum mechanicsMagnetic fieldElectron

Abstract

fetched live from OpenAlex

A recent experiment on the bulk compound 4Hb-TaS$_2$ reveals an unusual time-reversal symmetry-breaking superconducting state that possesses a magnetic memory not manifest in the normal state. Here we provide a mechanism for this observation by studying the magnetic and electronic properties of 4Hb-TaS$_2$. We discuss the criterion for a small magnetization in the normal state in terms of spin and orbital magnetizations. Based on an analysis of lattice symmetry and Fermi surface structure, we propose that 4Hb-TaS$_2$ realizes superconductivity in the interlayer, equal-spin channel with a gap function whose phase winds along the Fermi surface by an integer multiple of $6π$. The enhancement of the magnetization in the superconducting state compared to the normal state can be explained if the state with a gap winding of $6π$ is realized, accounting for the observed magnetic memory. We discuss how this superconducting state can be probed experimentally by spin-polarized scanning tunneling microscopy.

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 score0.002
Threshold uncertainty score0.006

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.226
GPT teacher head0.240
Teacher spread0.015 · 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
Published2023
Admission routes1
Has abstractyes

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