Effects of Mg and Al doping on dislocation slips in GaN
Bibliographic record
Abstract
First-principles density functional theory calculations were employed to systematically examine the effects of Mg and Al additions to wurtzite GaN on the generalized stacking fault energy (GSFE) curves for (11¯00)[112¯0] and (11¯00)[0001] dislocations along the glide or shuffle slip planes. It was found that for both slip systems, Mg doping leads to significant reduction of the GSFE while Al doping elevates the GSFE curve. For each dopant, the effect of doping on the GSFE was shown to scale linearly with the dopant concentration, being independent of the slip (i.e., glide or shuffle) plane. The GSFE curves were subsequently combined with the Peierls-Nabarro model to quantitatively analyze the micromechanical characteristics of dislocation slips. The implications of our findings to slip dynamics and dislocation dissociation mechanism were then discussed. Our study provides important insights towards the understanding and control of dislocation dynamics in impurity-doped GaN.
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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".