<i>(Invited)</i> Reconfigurable All-Nitride Magneto-Ionics
Bibliographic record
Abstract
The rapid advancement of generative artificial intelligence has significantly increased the demand for both energy and data storage. Magneto-ionics, which utilizes ionic motion to control magnetism, often driven by an electric field in heterostructures, has gained significant attention for its potential to enable energy-efficient modulation of magnetic properties with large effects. This study proposes a unique COMS-compatible solid-state magneto-ionic system composed of all Mn nitrides and demonstrates that nitrogen ionic motion can induce reversible phase transitions between ferrimagnetic and antiferromagnetic Mn nitrides. This magnetic phase transition is manifested in dramatic changes in the resultant exchange bias effect which can be increased by over an order of magnitude when more nitrogen is introduced into the nitrides during deposition and subsequently reduced by over 70% when nitrogen is taken out of the nitrides through post-annealing. Additionally, voltage-induced nitrogen ionic motion can lead to reversible changes in saturation magnetization and exchange bias effect by 23% and 0.1 T (16%) at 5 K, respectively. These findings highlight the potential of this all-Mn-nitride system as an industrially viable and environmentally sustainable platform, offering tunable magnetic properties and energy-efficient operation, and potential for magnetic field immunity. This work has been supported in part by the NSF (DMR-2005108, ECCS-2151809, ECCS-2132098) and KAUST (OSR-2019-CRG8-4081).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".