<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mtext>SU</mml:mtext> <mml:mo>(</mml:mo> <mml:mi>N</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> spin-phonon simulations of Floquet dynamics in spin <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>S</mml:mi> <mml:mo>></mml:mo> <mml:mfrac> <mml:mn>1</mml:mn> <mml:mn>2</mml:mn> </mml:mfrac> </mml:mrow> </mml:math> Mott insulators
Bibliographic record
Abstract
The dynamics of magnetic moments coupled to phonons is of great interest for understanding spin transport in solids as well as for our ability to control magnetism via tailored phonon modes. For spin $S > 1/2$, spin-orbit coupling permits an unusual linear coupling of phonons to quadrupolar moments, so that phonons act as a dynamical transverse field for the spins. Here, we develop a generalized $SU(N)$ spin-phonon Monte Carlo and molecular dynamics technique to simulate the equilibrium and nonequilibrium properties of such spin-orbital-phonon coupled Mott insulators, and apply it to a spin-1 model with competing XY antiferromagnet (AFM) and quadrupolar paramagnet (QPM) phases which is relevant to the Mott insulator $\rm{Ba_2FeSi_2O_7}$. We uncover a rich variety of dynamical phenomena in this system induced by linear or chiral phonon drives, including the generation of a uniform magnetization in the QPM and AFM, strengthening of Néel order and gapping of the AFM Nambu-Goldstone mode by Floquet-Ising anisotropy, a non-equilibrium QPM to AFM transition, and creation of Floquet copies of transverse and longitudinal spin waves. Our work is relevant for driven spin-1 magnets, such as $\rm{Ba_2FeSi_2O_7}$, and we highlight broader implications for nonequilibrium multipolar magnetism.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.493 | 0.374 |
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".