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Record W2156432983 · doi:10.48550/arxiv.1505.05351

Classification and Monte Carlo study of symmetric $Z_{2}$ spin liquids on the triangular lattice

2015· preprint· en· W2156432983 on OpenAlexfundno aff
Wayne Zheng, Jia‐Wei Mei, Yang Qi

Bibliographic record

VenuearXiv (Cornell University) · 2015
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicPhysics of Superconductivity and Magnetism
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaInstitut Périmètre de physique théoriqueNational Natural Science Foundation of ChinaIndustry CanadaTsinghua UniversityGovernment of Canada
KeywordsPhysicsVariational Monte CarloHexagonal latticeQuantum spin liquidWave functionSpinonCondensed matter physicsDiffusion Monte CarloPartonMonte Carlo methodLattice field theoryQuantum mechanicsGauge theoryQuarkSpin polarizationHybrid Monte CarloSuperconductivityMathematics

Abstract

fetched live from OpenAlex

We study different ways of symmetry fractionalization in $Z_{2}$ spin liquids on the triangular lattice. Our classification can be used to identify the symmetry fractionalization in the $Z_{2}$ spin liquid reported in recent density-matrix-renormalization-group simulations for $J_{1}-J_{2}$ spin model on the triangular lattice. We find 64 types of symmetry enriched $Z_{2}$ spin liquid states on triangular lattice. Besides 8 states constructed in Schwinger-boson parton wavefunctions, 12 more states can be realized in Abrikosov-fermion parton construction. Within a larger gauge group than SU(2), the rest 44 states are also found in a spin-3/2 system. Among 20 types of Abrikosov-fermion Gutzwillerprojected wavefunctions, No.B5 state is a promising candidate for the $Z_{2}$ spin liquid for $J_{1}-J_{2}$ spin model on the triangular lattice. No.B5 lies close to Dirac spin liquid (DSL). However, variational Monte Carlo simulation find that DSL has a good variational energy and $J_{1}-J_{2}$ spin model cannot open a gap for spinons on top of DSL to stabilize No.B5 state.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.890

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 head0.131
GPT teacher head0.220
Teacher spread0.089 · 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 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

Citations14
Published2015
Admission routes1
Has abstractyes

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