Understanding activation and growth of twin variants in polycrystalline magnesium under tension and compression: An atomistic study
Bibliographic record
Abstract
• MD simulation details twinning in polycrystalline Mg under uniaxial load. • An algorithm is developed to identify twin variants in deformed polycrystal. • Three-dimensional analysis shows distinct twin patterns in tension and compression. • Schmid factor's critical role in twin activation/selection is confirmed. Deformation twinning is known to be important in the acquisition of plasticity for hexagonal close-packed crystal structures, of great implication to the design and development of novel high-strength Mg alloys with enhanced formability. Accurate understanding of deformation twinning necessitates critical mechanistic knowledge of the activation and selection of twins at nanoscale. In this work, considering polycrystalline Mg, we performed comprehensive molecular dynamics simulations to investigate deformation twinning under uniaxial tension and compression loading. An algorithm has been developed and implemented to identify the active twin variants of three operating twin modes during deformation. Sharp contrast between tension and compression loading in terms of twin patterns and twin growth was observed, attributed to difference in twin variant activation and twin-twin interaction under the two loading conditions. Furthermore, the critical role of Schmid factor in twin variant activation and selection has been elucidated, in good agreement with experimental observations. This study contributes critical insights towards advancing our understanding of the complex behaviors of deformation twinning in polycrystalline Mg.
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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 | 0.000 | 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".