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Record W4377087030 · doi:10.1088/2515-7639/acd723

Renormalized phonon spectrum in the Su–Schrieffer–Heeger model

2023· article· en· W4377087030 on OpenAlexafffund
Stepan Fomichev, Mona Berciu

Bibliographic record

VenueJournal of Physics Materials · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsUniversity of British Columbia
FundersStewart Blusson Quantum Matter Institute, University of British ColumbiaNatural Sciences and Engineering Research Council of Canada
KeywordsRenormalizationPhononLattice (music)PhysicsQuadratic equationQuantum mechanicsCoupling constantCondensed matter physicsMathematical physicsMathematics

Abstract

fetched live from OpenAlex

Abstract Motivated to understand phonon spectrum renormalization in the ground state of the half-filled Su–Schrieffer–Heeger model, we use the Born–Oppenheimer approximation together with the harmonic approximation to evaluate semi-analytically the all-to-all real-space ionic force constants generated through both linear and quadratic electron-phonon coupling. We then compute the renormalized phonon spectrum and the corresponding lattice zero-point energy (ZPE) as a function of the lattice dimerization. Crucially, the latter is included in the system’s total energy, and thus has a direct effect on the equilibrium dimerization. We find that inclusion of a small quadratic coupling leads to very significant changes in the predicted equilibrium dimerization, calling into question the use of the linear approximation for this model. We also argue that inclusion of the ZPE is key for systems with comparable lattice and electronic energies, and/or for finite size chains. Our method can be straightforwardly generalized to study similar problems in higher dimensions.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.248
Teacher spread0.232 · 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 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

Citations2
Published2023
Admission routes2
Has abstractyes

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