Striped magnetic ground state on an ideal <i>S</i> = 2 Kagomé lattice
Bibliographic record
Abstract
We have used representational symmetry analysis of neutron powder diffraction data to determine the magnetic ground state of Fe4Si2Sn7O16. We recently reported a long-range antiferromagnetic (AFM) Nel ordering transition in this compound at TN = 3.0 K, based on magnetization measurements. [1] The only magnetic ions present are layers of highspin Fe2+ (d6, S = 2) arranged on a perfect kagom lattice (trigonal space group P-3m1). [2] Below TN = 3.0 K, the spins on 2/3 of these magnetic ions order into canted antiferromagnetic chains, separated by the remaining 1/3 which are geometrically frustrated and show no long-range ordered down to at least T = 0.1 K. Moessbauer spectroscopy shows that there is no static order on the latter 1/3 of the magnetic ions -i.e., they are in a liquid-like rather than a frozen statedown to at least 1.65 K. A heavily Mn-doped sample Fe1.45Mn2.55Si2Sn7O16 has the same ground state. Although the magnetic propagation vector k = (0, 1/2, 1/2) breaks hexagonal symmetry, we see no evidence for magnetostriction in the form of a lattice distortion within the resolution of our data. To the best of our knowledge, this type of magnetic order on a kagom lattice has no precedent experimentally and has not been explicitly predicted theoretically. We will discuss the relationship between our experimental result and a number of theoretical models that predict symmetry breaking ground states for perfect kagom lattices.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".