Overcoming the Barrier on Time Step Size in Multiscale Molecular Dynamics Simulation of Molecular Liquids
Bibliographic record
Abstract
We propose and validate a new multiscale technique, the extrapolative isokinetic Nóse-Hoover chain orientational (EINO) motion multiple time step algorithm for rigid interaction site models of molecular liquids. It nontrivially combines the multiple time step decomposition operator method with a specific extrapolation of intermolecular interactions, complemented by an extended isokinetic Nosé-Hoover chain approach in the presence of translational and orientational degrees of freedom. The EINO algorithm obviates the limitations on time step size in molecular dynamics simulations. While the best existing multistep algorithms can advance from a 5 fs single step to a maximum 100 fs outer step, we show on the basis of molecular dynamics simulations of the TIP4P water that our EINO technique overcomes this barrier. Specifically, we have achieved giant time steps on the order of 500 fs up to 5 ps, which now become available in the study of equilibrium and conformational properties of molecular liquids without a loss of stability and accuracy.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".