Generation of viable nanocrystalline structures using the melt-cool method: the influence of force field selection
Bibliographic record
Abstract
The influence of force field selection on the formation of nanocrystalline structures of pure copper using the Melt-Cool method within Molecular Dynamics (MD) was investigated. This is of utmost importance since the use of MD to investigate nanocrystalline materials is critical due to the ability to study on the appropriate length scale. The Melt-Cool simulation method involves annealing the starting single crystal structure to temperatures exceeding the melting point of the material, followed by rapid quenching and equilibration to room temperature. The heating and rapid quenching allows for the mixture of atoms to randomised orientations with realistic microstructures and randomised defects, such as interstitial or vacancy atoms. Due to the requirement of the mathematical model (force field) to predict the nanocrystalline structure, the influence of force field selection is of paramount importance – a major gap found in currently available literature. The current investigation was performed in two phases: initial investigation to understand influence of force field parameterisation on formation of nanocrystalline structures, followed with an investigation to enhance predictions of mechanical properties of nanocrystalline copper found in currently available literature. The initial phase demonstrated a clear dependence on force field selection for the formation of viable nanocrystalline structures. The second phase demonstrated that the most accurate force field for mechanical properties may not be the ideal selection for use with the Melt-Cool method. The most ideal force field selection when performing the Melt-Cool method with the goal of obtaining accurate mechanical properties for pure copper was determined.
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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) | 0.000 | 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".