ReaxFF Molecular Dynamics Simulation for the Graphitization of Amorphous Carbon: A Parametric Study
Bibliographic record
Abstract
A parametric study of ReaxFF for molecular dynamics simulation of graphitization of amorphous carbon was conducted. The responses to different initial amorphous carbon configurations, simulation time steps, simulated temperatures, and ReaxFF parameter sets were investigated. The results showed that a time step shorter than 0.2 fs is sufficient for the ReaxFF simulation of carbon using both Chenoweth 2008 and Srinivasan 2015 parameter sets. The amorphous carbon networks produced using both parameter sets at 300 K are similar to each other, with the first peak positions of pair distribution function curves located between the graphite sp 2 bond peak position and the diamond sp 3 bond peak position. In the graphitization process, the graphene fragment size increases and the orientation of graphene layers transforms to be parallel with each other with the increase of temperature and annealing time. This parallel graphene structure is close to the crystalline graphite. Associated with this graphitization is the presence of small voids and pores which arise because of the more efficient atomic packing relative to a disordered structure. For all initial densities, both potential parameter sets exhibit the expected behavior in which the sp 2 fraction increases significantly over time. The sp 2 fraction increases with increasing temperature. The differences of sp 2 fraction at different temperatures are more obvious in lower density at 1.4 g/cm 3 . When density is increased, the gap caused by different temperatures becomes small. This study indicates that both Chenoweth 2008 and Srinivasan 2015 potential sets are appropriate for molecular dynamics simulations in which the growth of graphitic structures is 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".