Lifshitz transition and triplet <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>p</mml:mi> </mml:math> -wave pairing from the induced ferromagnetic plaquette via spin differentiated nonlocal interaction
Bibliographic record
Abstract
We study the two-dimensional extended Hubbard model on a square lattice and incorporate spin-differentiated nearest neighbor (NN) interactions where the equal-spin (${V}_{uu}$) and unequal-spin (${V}_{ud}$) terms are independently tuned parameters. We compute single-particle excitations as well as static spin and pairing susceptibilities perturbatively up to fourth order within the thermodynamic limit and at a finite fixed temperature. By explicitly encoding a ferromagneticlike NN interaction (${V}_{uu}<{V}_{ud}$), we induce a competition among the uniform $q=(0,0)$, collinear $q=(\ensuremath{\pi},0)$, and staggered $q=(\ensuremath{\pi},\ensuremath{\pi})$ spin excitations. This results in the formation of short-ranged $2\ifmmode\times\else\texttimes\fi{}2$ ferromagnetic plaquettes arranged in staggered or striped patterns. Kinetic frustration in hopping, both within and between these plaquettes, manifests in single-particle properties, resulting in a reduction of bandwidth and ultimately triggering a Lifshitz transition to quasi-one-dimensional bands. Furthermore, an attractive effective interaction within the localized ferromagnetic plaquette results in the emergence of equal-spin triplet $p$-wave pairing. We demonstrate that sufficiently strong magnetic fluctuations, even at finite length scales, can significantly influence single-particle and pairing properties without breaking translational symmetry. Our approach provides a pathway to realize a variety of rich magnetic phases and Fermi surface reconstruction driven by interactions in the absence of explicit geometric frustration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".