Unconstrained magnetism in nanostructures at zero temperature: An ultimate goal
Bibliographic record
Abstract
Recent calculations have shown that the magnetization of nanostructures cannot be safely described by collinear models based on phenomenological Ising Hamiltonian or electronic structure approaches. When interactions between spins are screened by electronic clouds, a Heisenberg Hamiltonian presents a safe approach for ground state calculations as well as for the determination of temperature dependant magnetization. In metallic systems, due to strong interactions between spins, semi-empirical models like Extended Hückel, tight-binding or Periodic Anderson Model (PAM) have been used. Within these oversimplified approaches, vector magnetization could be tested and, for nanostructures, it generally led to non-collinear ground states. Ab initio calculations based on Kohn-Sham techniques can also describe non-collinear ground states but, because these techniques work in k-space, periodicity is necessary. This is a strong approximation for nanostructures. Therefore, in the present short review, we essentially focus on non-collinear magnetism of nanostructures by means of PAM approaches.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".