Development and assessment of atomistic models for predicting static friction coefficients
Bibliographic record
Abstract
The friction coefficient relates friction forces to normal loads and plays a key role in fundamental and applied areas of science and technology. Despite its importance, the relationship between the friction coefficient and the properties of the materials forming a sliding contact is poorly understood. We illustrate how simple relationships regarding the changes in energy that occur during slip can be used to develop a quantitative model relating the friction coefficient to atomic-level features of the contact. The slip event is considered as an activated process and the load dependence of the slip energy barrier is approximated with a Taylor series expansion of the corresponding energies with respect to load. The resulting expression for the load-dependent slip energy barrier is incorporated in the Prandtl-Tomlinson (PT) model and a shear-based model to obtain expressions for friction coefficient. The results indicate that the shear-based model reproduces the static friction coefficients ${\ensuremath{\mu}}_{s}$ obtained from first-principles molecular dynamics simulations more accurately than the PT model. The ability of the model to provide atomistic explanations for differences in ${\ensuremath{\mu}}_{s}$ amongst different contacts is also illustrated. As a whole, the model is able to account for fundamental atomic-level features of ${\ensuremath{\mu}}_{s}$, explain the differences in ${\ensuremath{\mu}}_{s}$ for different materials based on their properties, and might be also used in guiding the development of contacts with desired values of ${\ensuremath{\mu}}_{s}$.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 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".