An atomistic simulation study of the crystallographic orientation relationships during the austenite to ferrite transformation in pure Fe
Bibliographic record
Abstract
Molecular dynamics (MD) simulations on a model of pure Fe have been used in the investigation of solid-state nucleation of a body-centered-cubic (BCC) phase from a polycrystalline face-centered-cubic (FCC) matrix. A neighbor vector analysis (NVA) method has been introduced and it is shown how the NVA can be used to determine the misorientation of grain or interphase boundaries. In particular, the NVA was utilized to identify the orientation relationships (ORs) of several BCC nuclei and three special ORs were tested, namely the Kurdjumov-Sachs (KS), Nishiyama–Wassermann (NW) and Pitsch (P). From several quasi-2D simulations, it was found that all stable nuclei at grain boundaries formed at least one orientation relationship with the parent grains that was consistent with either the KS or NW relationship. Several initial MD simulation cells, which prohibited the formation of special ORs, were also examined and in these simulations no nucleation was observed after long run times. In addition, the orientation was detected in all mobile phase boundaries. Consistent with experimental findings, these observations demonstrate the importance of this high coherency atomic plane during both the nucleation and growth process. The nucleation and phase boundary characteristics identified here may provide important insights into the nucleation rate and grain orientation of more general solid state nucleation processes.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".