Monte Carlo simulations of inter- and intra-grain spin structure of Ising and Heisenberg models
Bibliographic record
Abstract
In order to keep supplying computer hard disk drives with more and more storage space, it is essential to have smaller bits. With smaller bits, superparamagnetism, the spontaneous flipping of the magnetic moments in a bit caused by thermal fluctuations, becomes increasingly important and impacts the stability of stored data. Recording media is composed of magnetic grains (usually made of CoCrPt alloys) roughly 10nm in size from which bits are composed. Most modeling efforts that study magnetic recording media treat the grains as weakly interacting uniformly magnetized objects. In this work, the spin structure internal to a grain is examined along with the impact of varying the relative strengths of intra-grain and inter-grain exchange interactions. The interplay between these two effects needs to be examined for a greater understanding of spuerparamagnetism as well as for the applications of the proposed Heat Assisted Magnetic Recording (HAMR) technology where thermal fluctuations facilitate head-field induced bit reversal in high anisotropy media. -- Simulations using the Monte Carlo method (with cluster-flipping algorithms) are performed on a 2D single-layer and multilayer Ising model with a strong intra-grain exchange interaction J as well as a weak inter-grain exchange J’. A strong deviation from traditional behavior is found when J’/J is significant. M-H hysteresis loops are also calculated and the coercivity, Hc, is estimated. A large value represents a strong resilience to the superparamagnetic effect. It is found that taking into account the internal degrees of freedom has a significant effect on Hc. As the Ising model serves only as an approximation, preliminary simulations are also reported on a mre realistic Heisenberg model with u;niaxial anisotropy.
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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 | 0.003 |
| 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) | 0.003 | 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".