Molecular force fields for aqueous electrolytes: SPC/E-compatible charged LJ sphere models and their limitations
Bibliographic record
Abstract
Thirteen of the most common aqueous NaCl solution force fields based on the SPC/E water solvent are examined with respect to their prediction at ambient conditions of the concentration dependence of the total electrolyte chemical potential and the solution density. We also calculate the salt solubility and the chemical potential and density of the NaCl crystalline solid. We obtain the solution chemical potential in a computationally efficient manner using our recently developed Osmotic Ensemble Monte Carlo method [F. Moučka, M. Lísal, and W. R. Smith, J. Phys. Chem. B 116, 5468 (2012)]. We find that the results of the force fields considered are scattered over a wide range of values, and none is capable of producing quantitatively accurate results over the entire concentration range, with only two of them deemed to be acceptable. Our results indicate that several force fields exhibit precipitation at concentrations below the experimental solubility limit, thus limiting their usefulness. This has important implications, both in general and for their use in biomolecular simulations carried out in the presence of counter-ions. We conclude that either different parameter fitting techniques taking high-concentration properties into account must be used when determining force field model parameters, or that the class of models considered here is intrinsically incapable of the task and more sophisticated mathematical forms must be used.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".