Mechanism for switchability in electron-doped ferroelectric interfaces
Bibliographic record
Abstract
With the recent experimental verification that ferroelectric lattice distortions survive in the metallic phase of some materials, there is a desire to create devices that are both switchable and take advantage of the novel functionalities afforded by polar interfaces. In this work, we explore a simple model for such an interface and demonstrate a mechanism by which a metallic ferroelectric substrate may be switched by a bias voltage. This finding is in contrast to the reasonable expectation that hysteresis is prevented by screening of external fields in ferroelectric metals. Instead, the electron gas binds to polarization gradients to form a compensated state. Uncompensated electrons, which may screen external fields, are generated either when the electron density exceeds the ferroelectric polarization or when the bias voltage exceeds a ``spillover'' threshold. We propose that switchable thin films may be optimized by choosing an electron density that is slightly less than the lattice polarization. In addition to the high-polarization states, we find that thin metallic films also have a low-polarization state with average polarization near zero. Unlike in insulating films, where the polarization is small everywhere in this state, the low-polarization state in the metallic films consists of two head-to-head domains of opposite polarization. This domain formation is enabled by the screening of depolarizing fields by the electron gas.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".