Dislocation-mediated plasticity in silicon during nanometric cutting: A molecular dynamics simulation study
Bibliographic record
Abstract
The nucleation and propagation of dislocations and its consequence on the defect structure in silicon during nanometric cutting are not well known, although the amorphization and high pressure phase transformation studies on silicon have remained at the epicentre of research across various disparate disciplines for over a decade. This paper proposes a new mechanism of crystal plasticity identified by a fully automated dislocation extraction algorithm in molecular dynamics simulations of nanometric cutting of silicon for different cutting planes/directions at a wide range of temperatures (300–1500 K). Alongside amorphization of silicon, our simulations revealed nanoscale stochastic nucleation of dislocations and stacking faults, which serve as mediators of microscopic plasticity during various contact loading operations and manufacturing processes of silicon. Of interest is that, irrespective of the cutting temperature, the stacking faults, which were not formed for either the (010)[100] or (111)[1̅10] crystal setups, were generated with three atomic layers in the (110)[001̅] cutting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".