A Cumulative Approach to Crystalline Structure Characterization in Atomistic Simulations
Bibliographic record
Abstract
Crystalline characterization poses a challenge when atomic deformation and phase transformation are occurring in an atomic simulation. Crystalline solids are typically characterized by parameters used to classify local atomic arrangements in order to extract features such as crack tips, dislocations, and free surfaces. One such characterization parameter, the common neighborhood parameter (CNP), has been used as an approach to characterize those features with an enhanced formulation applicable to non-monoatomic interactions. The present work introduces a novel approach that extends the CNP to characterize crystalline structures by means of cumulative common neighborhood parametrization (CCNP) for arbitrary structures. The method is compared with the centrosymmetry parameter (CSP) and the common neighborhood parameter (CNA). The methods were applied to a molecular dynamics (MD) simulation of uniaxial tension in an aluminum nanowire. The results showed CCNP’s superior performance in detecting distinct surface features from bulk features with excellent parameter value ranges. The method was also extended to characterize a complex P 4 2 / mnm space group, non-monoatomic crystal with no common first-nearest neighbors in a type I MD fracture simulation. The data refinement of the proposed method was applied to extract surface features like edges, roughness, corner, and undeformed surface atoms.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".