Simulation on the Factors Affecting the Crystallization Process of FeNi Alloy by Molecular Dynamics
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide This paper investigates the crystallization process of FeNi alloys with different impurity concentrations of Ni( x ) [ x = 10% (Fe 90 Ni 10 ), 20% (Fe 80 Ni 20 ), 30% (Fe 70 Ni 30 ), 40% (Fe 60 Ni 40 ), and 50% (Fe 50 Ni 50 )] at temperature ( T ) = 300 K and Fe 70 Ni 30 at heating rates of 4 × 10 12, 4 × 10 13, and 4 × 10 14 K/s at different temperatures, T = 300, 400, 500, 600, 700, 900, 1100, and 1300 K. Molecular dynamics models with the Sutton–Chen embedded interaction potential and recirculating boundary conditions are used to calculate the molecular parameters of alloys, such as radial distribution function, total energy of the system ( E tot ), size ( l ), and crystallization temperature (through the relationship between E tot and T ). The common neighborhood analysis method is used to confirm the theoretical results of crystallization for Fe–Fe, Fe–Ni, and Ni–Ni. The annealing process did not have an effect on the crystallization process of FeNi alloys. The effect of Ni content, heating rate, and annealing time on structural unit numbers, such as face-centered cubic, hexagonal close-packed, blocked cubic center, and amorphous, and the crystallization process of FeNi alloys is also investigated.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".