Comparison of simulation and experimental results for a model aqueous tert-butanol solution
Bibliographic record
Abstract
Molecular dynamics simulations are used to investigate the behavior of aqueous tert-butanol (TBA) solutions for a range of temperatures, using the CHARMM generalized force field (CGenFF) to model TBA and the TIP4P/2005 or TIP4P-Ew water model. Simulation results for the density, isothermal compressibility, constant pressure heat capacity, and self-diffusion coefficients are in good accord with experimental measurements. Agreement with the experiment is particularly good at low TBA concentration, where experiments have revealed anomalies in a number of thermodynamic properties. Importantly, the CGenFF model does not exhibit liquid-liquid demixing at temperatures between 290 and 320 K (for systems of 32 000 molecules), in contrast with the situation for several other common TBA models [R. Gupta and G. N. Patey, J. Chem. Phys. 137, 034509 (2012)]. However, whereas real water and TBA are miscible at all temperatures where the liquid is stable, we observe some evidence of demixing at 340 K and above. To evaluate the structural properties at low concentrations, we compare with both neutron scattering and recent spectroscopic measurements. This reveals that while the CGenFF model is a definite improvement over other models that have been considered, the TBA molecules still exhibit a tendency to associate at low concentrations that is somewhat stronger than that indicated by experiments. Finally, we discuss the range and decay times of the long-range correlations, providing an indication of the system size and simulation times that are necessary in order to obtain reliable results for certain properties.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".