Stacking-Dependent Interlayer Magnetic Coupling in 2D CrI <sub>3</sub> /CrGeTe <sub>3</sub> Nanostructures for Spintronics
Bibliographic record
Abstract
The recent emergence of two-dimensional (2D) materials with intrinsic long-range magnetic order opens the avenue of fundamental physics studies and the spintronics application; however, the mechanism of interlayer magnetic coupling and the feasible way to control magnetic states are yet to be fully investigated. In the present study, from first-principle calculations, we studied the interlayer magnetic coupling of 2D CrI 3 /CrGeTe 3 heterostructures and revealed the stacking-dependent magnetic states. It is found that AB and AB 1 stacking are prefer ferromagnetic interlayer coupling, while the other two stacked configurations are in the ferrimagnetic state. The underlying mechanism has contributed to the competition between nearest-neighbor (NN) and second-nearest-neighbor (SNN) Cr–Cr atoms interaction between layers. Meanwhile, it is also found that the electronic properties are stacking dependent, while the band edge states are separated to the different layers. The magnetic and electronic states can be effectively tuned by the external strain. Based on these findings, the magnetic domain devices are proposed in the twisted magnetic heterostructures with the domain size and interlayer coupling being controlled by the rotation angle. Our study thus provides an approach to achieve the controllable magnetic/electronic properties which is not only important for fundamental research but also useful for the practical applications in spintronics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".