Influence Of Forcefield Selection On The Formation Of Viable Nanocrystalline Copper Structures Using The Melt Cool Method
Bibliographic record
Abstract
The influence of forcefield selection on the development of a viable nanocrystalline structure using Molecular Dynamics (MD) is of utmost importance as researchers identify enhanced methods for creating realistic microstructures. One such method for creating nanocrystalline structures which possess realistic microstructures with randomized defects is the Melt Cool method. The simulation process involves annealing the starting single crystal structure to temperatures which exceed the melting point of the metal, followed by a rapid quench and equilibration to room temperature which allows for the formation of nanocrystalline grains. However, the influence forcefields have on the formation of viable structures is not discussed in currently available studies found in literature. Many studies fail to demonstrate the effect various forcefield fitting parameters have on the formation of viable structures using the Melt Cool method. Therefore, there is a deficiency of valuable information available for researchers working in the forcefield development. Moreover, a disservice is not only given to forcefield development, but also future research on more complicated nanocrystalline materials. As such, the current investigation was performed to highlight the influence forcefield selection has on the formation of viable nanocrystalline structures of pure copper. It was shown that the forcefield has a direct impact on the formation of said structures and it is of utmost importance to provide valuable data for the development of future forcefields.
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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".