Examination of critical grain size of isotropic nanocrystalline iron through molecular dynamics analysis
Bibliographic record
Abstract
The critical grain size for isotropic nanocrystalline pure iron was investigated utilizing Molecular Dynamics (MD). First, number of grains required for isotropic behaviour was determined utilizing statistically significant grain counts – alleviating challenges faced in simulation of nanocrystalline materials. The current investigation provides a thorough guideline for simulating isotropic nanocrystalline materials. Second, an investigation into the effect of strain rate was performed to demonstrate its effect on mechanical properties of pure, isotropic nanocrystalline iron. Next, the critical grain size of pure nanocrystalline iron was investigated, indicating the change from Hall-Petch to inverse Hall-Petch. It was shown that MD of an isotropic pure nanocrystalline iron structure possessed a clearly defined critical grain size through examination of flow stress and maximum stress. It was shown that neither elastic modulus nor Poisson’s ratio were viable indicators for a critical grain size, instead, both tended towards their macroscale equivalent. Alternatively, yield stress may be viable, but was not recommended due to varying definitions for what exactly constitutes the yield point. Lastly, an investigation into the microstructure was performed near the critical grain size and at the extremes of grain sizes investigated. This highlighted the microstructural behaviour within both Hall-Petch and inverse Hall-Petch regimes throughout straining.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".