Considerations when calculating the mechanical properties of single crystals and bulk polycrystals from molecular dynamics simulations
Bibliographic record
Abstract
The choice of a proper interatomic potential is critical to obtaining accurate and realistic molecular dynamics results. However, previous studies that have tested the suitability of a potential to predict mechanical properties often do so using elastic constants from a triaxial stress state that ignores Poisson’s effect. While this method is suitable it is not consistent with macroscale experimental methods and cannot provide the complete loading behaviour. Further, there is a lack of knowledge as to whether accuracy in predicting elastic constants from a fixed volume condition indicates accuracy for elastic moduli from uniaxial tensile simulations. Moreover, those studies that did account for Poisson’s effect studied only one crystal orientation and thus assumed potential accuracy is independent of crystal orientation. Results from the current study demonstrated that accuracy of a potential is dependent on the crystal direction. Further, the most accurate potentials for elastic constants calculated using a fixed volume condition were not necessarily the most accurate at predicting elastic moduli from a physically realisable tension test. Finally, the Voigt Reuss Hill (VRH) method was shown to accurately predict polycrystalline mechanical properties from single crystal data as a function of temperature.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".