Strain and orientation engineering in ABO<sub>3</sub> perovskite oxide thin films
Bibliographic record
Abstract
are widely studied for their properties including ferroelectricity, magnetism, strongly correlated physics, optical effects, and superconductivity. A thriving research direction using such materials is through their integration as epitaxial thin films, allowing many novel and exotic effects to be discovered. The integration of the thin film on a single crystal substrate, however, can produce unique and powerful effects, and can even induce phases in the thin film that are not stable in bulk. The substrate imposed mechanical boundary conditions such as strain, crystallographic orientation, octahedral rotation patterns, and symmetry can also affect the functional properties of perovskite films. Here, the author reviews the current state of the art in epitaxial strain and orientation engineering in perovskite oxide thin films. The paper begins by introducing the effect of uniform conventional biaxial strain, and then moves to describe how the substrate crystallographic orientation can induce symmetry changes in the film materials. Various material case studies, including ferroelectrics, magnetically ordered materials, and nonlinear optical oxides are covered. The connectivity of the oxygen octahedra between film and substrate depending on the strain level as well as the crystallographic orientation is then discussed. The review concludes with open questions and suggestions worthy of the community's focus in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".