Recent Progress in Molecular Simulation of Aqueous Electrolytes: Force Fields, Chemical Potentials and Solubility
Bibliographic record
Abstract
Although aqueous electrolytes are among the most important solutions, the molecular simulation of their intertwined properties of chemical potentials, solubility and activity coefficients has remained a challenging problem, and has attracted considerable recent interest. In this perspectives review, we focus on the simplest case of aqueous sodium chloride at ambient conditions and discuss the two main factors that have impeded progress. The first is lack of consensus with respect to the appropriate methodology for force field (FF) development. We examine how most commonly used FFs have been developed, and emphasize the importance of distinguishing between "Training Set Properties" used to fit the FF parameters, and "Test Set Properties", which are pure predictions of additional properties. The second is disagreement among solubility results obtained, even using identical FFs and thermodynamic conditions. Solubility calculations have been approached using both thermodynamic--based methods and direct molecular dynamics--based methods implementing coexisting solution and solid phases. Although convergence has been very recently achieved among results based on the former approach, there is as yet no general agreement with simulation results based on the latter methodology. We also propose a new method to directly calculate the electrolyte standard chemical potential in the Henry-Law ideality model. We conclude by making recommendations for calculating solubility, chemical potentials and activity coefficients, and outline a potential path for future progress.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".