Strain-induced vertical self-organization of semiconductor quantum dots: A computational study
Bibliographic record
Abstract
Atomistic strain simulations based on the valence force field method are employed to study the vertical arrangements of semiconductor quantum dot (QD) multilayers. The effects of the QD shape, dimensions, and materials parameters are systematically investigated, varying independently the following parameters: spacer width H, QD lateral spacing D, base b, and height h, slope of the side facets, elastic properties of the dot and the substrate materials, and lattice mismatch between the dot and the substrate. The transition between vertically aligned and anti-aligned structures is found to be determined mainly by the ratios H/D and b/D, as well as by the strain anisotropy of the substrate and to a lesser extent of the QD. The dependence on the QD height h is significant only for steep side facets and large aspect ratios h/b, and the effects of the lattice mismatch strain and the bulk elastic moduli are found to be negligible. The comparison with experimental data shows an excellent agreement with the results from the simulations, demonstrating that the presented analysis results in precise theoretical predictions for the vertical self-organization regime in a wide range of QD materials systems.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".