Sampling Phase Space by a Combined QM/MM ab Initio Car−Parrinello Molecular Dynamics Method with Different (Multiple) Time Steps in the Quantum Mechanical (QM) and Molecular Mechanical (MM) Domains
Bibliographic record
Abstract
This study considers a scheme for sampling phase space in large molecules based on Car−Parrinello ab initio molecular dynamics. The scheme makes use of a combined quantum mechanics and molecular mechanics (QM/MM) method augmented with a multiple-time-step technique. This scheme makes it possible to oversample the computationally less expensive MM region relative to the QM domain. The goal here is to provide better ensemble averaging in the MM region that is usually larger in size and therefore typically has a higher degree of configurational variability. It is shown that the multiple-time-step integrator will generate the same trajectory as a standard molecular dynamics integrator. Moreover, with a gradual rescaling of masses, the energy conservation of a multiple-time-step simulation can be satisfied to the same extent as a standard simulation. Finally, it is demonstrated that the multiple-time-step QM/MM method can accelerate the equilibration and configurational sampling of a molecular dynamics simulation as it is used in thermodynamic integration. The scheme is not intended as a tool for generating trajectories in actual dynamics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".