A method for creating a non-equilibrium NT(P1−P2) ensemble in molecular dynamics simulation
Bibliographic record
Abstract
A method is proposed for creating a non-equilibrium ensemble with a constant number of molecules, constant temperature and constant pressures with different target values in two reservoirs [referred to as NT(P(1)-P(2)) ensemble] that are connected by a finite length nanopore. This method includes two steps. The first step places a partition between the two reservoirs and then creates a static pressure field and a proper system volume by using two self-adjusting plates on which two external forces/pressures with different target values are exerted. The second step removes the partition and the two self-adjusting plates and the pressure difference between the two reservoirs is maintained by a "pump" designed to simultaneously create a periodic boundary condition between the two reservoirs and supply the necessary force (work) to a subset of molecules for a steady state flow. To examine this method, several cases using liquid argon with a truncated and shift Lennard-Jones potential under different target pressures and pump sizes were studied. Results show that the method proposed in this paper works well. In addition, the method proposed in this paper was compared with the other external force field methods. The results show that as long as the external force is applied to a restricted set of molecules away from the channel a constant pressure difference between two reservoirs is maintained. The advantage of the algorithm proposed here also sets the absolute pressures with different target levels in two reservoirs instead of it being arbitrary. Studies show that the fluid flow rate or permeability through a nanopore depends not only on the pressure difference between two reservoirs, but also on the absolute pressures in two reservoirs.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".