Strain Engineering in Halide Perovskites
Bibliographic record
Abstract
Despite the well-known implications in the field of III–V semiconductors, lattice strain in halide perovskite materials has been largely overlooked until recently. Here, we review the effect of lattice strain on the structural, chemical, and optoelectronic properties of metal halide perovskites to understand how strain engineering can be applied to improve device performance. We start by arguing that perovskites, like any other semiconducting material, are not immune to the negative effects of mismanaged strain. We analyze the origin—and detrimental consequences—of lattice strain in perovskite crystals and heterostructures. We then discuss how strain management addresses the polymorphism issue of some of the most desirable perovskite compositions, and how it prevents the harmful migration of ions in perovskites. We conclude by offering our perspective on the unexplored potential of strain engineering and argue that its controlled management can lead to untapped territories, including perovskite large-area single-crystalline thin films and electrically pumped lasers.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".