Non-bonded force field for the interaction between metals and organic molecules: a case study of olefins on aluminum
Bibliographic record
Abstract
In this work, we parameterize an empirical potential for the interaction between organic molecules and metal surfaces via force matching. This is done by pursuing a self-consistent approach similar to the ones used for equilibrium simulations; however, special attention is paid to the suitability of the resulting potential for tribological (non-equilibrium) situations. Specifically, we study olefin molecules confined between two aluminum surfaces under realistic pressures and shear rates. We find that the Buckingham potential produces better agreement with the first principle data than other force fields. While our training set only contains hexene molecules, we find that the standard error in the fitted olefin-aluminum interaction increases only by a factor of 1.15 when the force field is applied to butene, octene, and decene. Including mirror charges into the treatment only marginally improves fits. While olefins on aluminum is merely a special case, the proposed methodology can be used to parameterize any other interaction between polymers and metal surfaces for use in tribological simulations.
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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) | 0.000 | 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".