Evaluations of Molecular Dynamics Methods for Thermodiffusion in Binary Mixtures
Bibliographic record
Abstract
The objective of this paper is to investigate the behavior of two well-known boundary-driven molecular dynamics (MD) approaches, namely, reverse nonequilibrium molecular dynamics (RNEMD) and heat exchange algorithm (HEX), as well as introducing a modified HEX model (MHEX) that is more accurate and computationally efficient to simulate the mass and heat transfer mechanism. For this investigation, the following binary mixtures were considered: one equimolar mixture of argon (Ar) and krypton (Kr), one nonequimolar liquid mixture of hexane (nC6) and decane (nC10), and three nonequimolar mixtures of pentane (nC5) and decane. In estimating the Thermodiffusion factor in these mixtures using the three methods, it was found that consistent with the findings in the literature, RNEMD predictions have the largest error with respect to the experimental data. Whereas, the MHEX method proposed in this work is the most accurate, marginally outperforming the HEX method. Most importantly, the computational efficiency of MHEX method is the highest, about 7% faster than the HEX method. This makes it more suitable for integration with multiscale computational models to simulate thermodiffusion in a large system such as an oil reservoir.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".