Predictive modeling of misfit dislocation induced strain relaxation effect on self-rolling of strain-engineered nanomembranes
Bibliographic record
Abstract
Combining atomistic simulations and continuum modeling, the effects of misfit dislocations on strain relaxation and subsequently self-rolling of strain-engineered nanomembranes have been investigated. Two representative material systems including (GaN/In0.5Ga0.5N) of wurtzite lattice and II–VI materials (CdTe/CdTe0.5S0.5) of zinc-blend lattice were considered. The atomistic characteristics of dislocation and the resulting lattice distorting were first determined by generalized-stacking-fault energy profile and disregistry function obtained through Peierls-Nabarro model. Those properties were then used to calculate the accurate mismatch strain of those nanomembranes with the presence of dislocations, and as inputs into von-Karman shell theory to quantitatively evaluate the effects on self-rolling curvature and anisotropy. The theoretical results were further confirmed by atomistic simulations of different crystal geometries and dislocation configurations. Our results provide essential theoretical insights towards prediction and design of rollup configurations for strain-engineered nanomembranes containing crystalline defects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".