Bibliographic record
Abstract
We present an introductory review of concepts behind micromagnetic simulations, in which magnetic moments representing collections of atomic spins within a material evolve according to the Landau-Lifshitz-Gilbert equation, a generalized torque equation. This evolution is determined by a variety of interactions, including those arising from external fields, magnetostatic and exchange effects, and magnetic anisotropy. Anisotropy is a key ingredient in the Stoner-Wohlfarth model, which provides a quantitative basis for understanding magnetic hysteresis. In turn, hysteresis loops provide a basis for comparing simulations and experiments, and are important, for example, in quantifying the heating response of a sample to an oscillating external magnetic field. Micromagnetic simulations bear conceptual similarity to molecular dynamics (MD) simulations, but whereas in MD classical potentials are used to naturally model interactions between atoms and/or molecules, the choice of modelling length scale in micromagnetics is less obvious. If effective interactions are determined for, say, two crystallographic unit cells of a material, how interaction parameters should scale with micromagnetic simulation cell size, particularly at finite temperature, is still an area of research. Finally, we discuss the coupling of magnetic and mechanical degrees of freedom in simulating atomic and nanoparticle systems. This review is based, in part, on our own experience in modelling hysteretic heating of magnetite nanoparticles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".