Fractionalized Magnetization Plateaus in the Shastry-Sutherland Lattice Material Er$_2$Be$_2$GeO$_7$
Bibliographic record
Abstract
The experimental study of magnetism on the Shastry-Sutherland lattice has been ongoing for more than two decades, following the discovery of the first Shastry-Sutherland lattice materials SrCu$_2$(BO$_3$)$_2$. However, the study of Shastry-Sutherland systems is often complicated by the requirements of high magnetic fields ($>$~20~T SrCu$_2$(BO$_3$)$_2$) or the presence of itinerate electrons (e.g. REB$_4$). In this paper, we present the magnetic properties of the Shastry-Sutherland lattice material Er$_2$Be$_2$GeO$_7$. Like SrCu$_2$(BO$_3$)$_2$, Er$_2$Be$_2$GeO$_7$ exhibits fractionalized magnetization plateaus. Unlike SrCu$_2$(BO$_3$)$_2$, Er$_2$Be$_2$GeO$_7$ exhibits long-range order below $\sim1~$K, and the plateaus are accessible using commercial laboratory equipment, occurring for fields <~1~T. The fractions of magnetization present are closest to $\frac{1}{4}$ and $\frac{1}{2}$ of the full powder moment; we show that the $\frac{1}{4}$ magnetization plateau in Er$_2$Be$_2$GeO$_7$ has a classical analog, well represented by the magnetic structure (canted antiferromagnetic) observed in powder neutron diffraction. The lack of itinerate electrons, chemical disorder, and the low fields required to access the fractionalized magnetization plateaus promises Er$_2$Be$_2$GeO$_7$ to be a prime candidate for the study of frustrated magnetism on the Shastry-Sutherland lattice.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".