Detailed analysis of magnetism in Ru monolayers
Bibliographic record
Abstract
We study the magnetic behavior of a 4d transition-metal Ru monolayer (ML) on different substrates and orientations. In the ground state, an ideal Ru-ML is expected to be ferromagnetic on Ag(001) and Au(001) with a magnetic moment, µ, around 1.7 µ B (Bohr magnetons) in both cases. On Cu(001), a Ru-ML is not magnetic. In this paper, we study the magnetic behavior of a Ru-ML at other orientations, i.e., (110) and (111), and analyze the phenomenon as a whole. We find magnetism on Au(111), and Ag(111) (µ ≈ 1 µ B for both) but no magnetic activity on a Cu substrate in any orientation. This gives the first impression that the lattice parameter of the substrate is the one that governs the switching on of magnetism in the Ru-ML. But then, we find that on both Ag(110) and Au(110), an ideal Ru-ML is not magnetic. For that reason, we have tried to find another geometric parameter that would correlate better with the magnetic moment. We find that neither the total number of first nearest neighbors for each orientation, nor the number of them on the ML, or the number of them in the substrate, or the area per atom on the ML, correlate. We find a correlation with a parameter, Ω 0 , that represents the volume per atom in the monolayersubstrate interface region. But this parameter seems to have the wrong trend according to intuition. Further analysis shows that the details of the interaction are important, and that the physics underlying the switching on of magnetism in a Ru-ML on noble metal substrates, is determined by an intraband transfer of d-electronic states, from lower and higher energies to the Fermi level, that enhances the density of states at that energy, in an important way. This depends on specificities of the interaction between the ML and the substrate that are hardly taken into account by a single parameter that is merely geometric. PACS Nos.: 75.10b, 75.30m, 75.70.Ak, 73.20.At
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".