Ferromagnetic polar metals via epitaxial strain: A case study of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>SrCoO</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math>
Bibliographic record
Abstract
While polar metals are a metallic analog of ferroelectrics, magnetic polar metals can be considered as a metallic analog of multiferroics. There have been a number of attempts to integrate magnetism into a polar metal by synthesizing new materials or heterostructures. Here we use a simple yet widely used approach---epitaxial strain in the search for intrinsic magnetic polar metals. Via first-principles calculations, we study strain engineering of a ferromagnetic metallic oxide ${\mathrm{SrCoO}}_{3}$, whose bulk form crystallizes in a cubic structure. We find that under an experimentally feasible biaxial strain on the $ab$ plane, collective Co polar displacements are stabilized in ${\mathrm{SrCoO}}_{3}$. Specifically, a compressive strain stabilizes Co polar displacements along the $c$ axis, while a tensile strain stabilizes Co polar displacements along the diagonal line in the $ab$ plane. In both cases, we find an intrinsic ferromagnetic polar metallic state in ${\mathrm{SrCoO}}_{3}$. In addition, we also find that a sufficiently large biaxial strain ($>4%$) can yield a ferromagnetic-to-antiferromagnetic transition in ${\mathrm{SrCoO}}_{3}$. Our work demonstrates that in addition to yielding emergent multiferroics, epitaxial strain is also a viable approach to inducing magnetic polar metallic states in quantum materials.
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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".