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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".