Molecular Simulation of Aqueous Electrolyte Solubility. 3. Alkali-Halide Salts and Their Mixtures in Water and in Hydrochloric Acid
Bibliographic record
Abstract
We extend the osmotic ensemble Monte Carlo (OEMC) molecular simulation method (Moučka et al. J. Phys Chem. B 2011, 115, 7849-7861) for directly calculating the aqueous solubility of electrolytes and for calculating their chemical potentials as functions of concentration to cases involving electrolyte hydrates and mixed electrolytes, including invariant points involving simultaneous precipitation of several solutes. The method utilizes a particular semigrand canonical ensemble, which performs simulations of the solution at a fixed number of solvent molecules, pressure, temperature, and specified overall electrolyte chemical potential. It avoids calculations for the solid phase, incorporating available solid chemical potential data from thermochemical tables, which are based on well-defined reference states, or from other sources. We apply the method to a range of alkali halides in water and to selected examples involving LiCl monohydrate, mixed electrolyte solutions involving water and hydrochloric acid, and invariant points in these solvents. The method uses several existing force-field models from the literature, and the results are compared with experiment. The calculated results agree qualitatively well with the experimental trends and are of reasonable accuracy. The accuracy of the calculated solubility is highly dependent on the solid chemical potential value and also on the force-field model used. Our results indicate that pairwise additive effective force-field models developed for the solution phase are unlikely to also be good models for the corresponding crystalline solid. We find that, in our OEMC simulations, each ionic force-field model is characterized by a limiting value of the total solution chemical potential and a corresponding aqueous concentration. For higher values of the imposed chemical potential, the solid phase in the simulation grows in size without limit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".