Chemical ordering phenomena in nanostructured FePt: Monte Carlo simulations
Bibliographic record
Abstract
Free-surface-induced L10 chemical long-range ordering phenomena in a nanolayer, a nanowire and a cubic nanoparticle of FePt were studied by means of Monte Carlo simulations. The system was modeled with nearest-neighbor and next-nearest-neighbor interatomic pair interactions deduced from ab initio calculations. The generated samples, the dimensionality of which was determined by appropriate periodic boundary conditions imposed upon the generated supercells, were initially either perfectly ordered in the c-variant L10 superstructure ((001)-oriented monatomic planes), or completely disordered in the fcc crystalline structure. Vacancy-mediated creation of equilibrium atomic configurations was modelled by relaxing the systems at temperatures below the 'order-disorder' transition point using the Glauber algorithm implemented with the vacancy mechanism of atomic migration. The (100)-type-surface-induced heterogeneous nucleation of L10-order domains was observed and quantified by means of an original parameterization enabling selective determination of volume fractions of particular L10-variants. Due to the specific competition between the three kinds of (100)-type free surfaces, the initial c-L10 variant long-range order appeared to be the most stable in the cubic nanoparticle. The initially disordered samples were transformed by the creation of a specific L10 domain structure with a mosaic of particular L10-variant domains at the surfaces and almost homogeneous long-range order in the inner volume. The analysis of correlation effects revealed that chemical ordering was initiated at the free surfaces.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".