The effect of many-body potential type and parameterisation on the accuracy of predicting mechanical properties of aluminium using molecular dynamics
Bibliographic record
Abstract
As opposed to traditional laboratory testing, Molecular Dynamics (MD) offers an atomistic scale method to estimate the mechanical properties of metals. However, there is limited literature that shows the effect of interatomic potentials when determining mechanical properties. Hence, the present research was conducted to investigate the accuracy of various interatomic potentials in estimating mechanical properties of aluminium. Several types of potentials, including Embedded Atom Method (EAM), Modified EAM (MEAM) and Reactive Force Field (ReaxFF) were compared with available experimental data for pure aluminium to determine the most accurate interatomic potential. A uniaxial tensile test was performed at room temperature using MD simulations for nanoscale aluminium. Results demonstrated that those potentials parameterised with elastic constants at physically realisable temperatures were consistently more accurate. Overall, the Mishin et al. EAM potential was the most accurate when compared to single-crystal experimental values. Regardless of the potential type, the error was significantly higher for those potentials that did not consider elastic constants during development. In brief, the application of the interatomic potentials to estimate mechanical properties of a nanoscale aluminium was 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".