Molecular Simulation of Aqueous Electrolyte Solubility. 2. Osmotic Ensemble Monte Carlo Methodology for Free Energy and Solubility Calculations and Application to NaCl
Bibliographic record
Abstract
We present a new and computationally efficient methodology using osmotic ensemble Monte Carlo (OEMC) simulation to calculate chemical potential-concentration curves and the solubility of aqueous electrolytes. The method avoids calculations for the solid phase, incorporating readily available data from thermochemical tables that are based on well-defined reference states. It performs simulations of the aqueous solution at a fixed number of water molecules, pressure, temperature, and specified overall electrolyte chemical potential. Insertion/deletion of ions to/from the system is implemented using fractional ions, which are coupled to the system via a coupling parameter λ that varies between 0 (no interaction between the fractional ions and the other particles in the system) and 1 (full interaction between the fractional ions and the other particles of the system). Transitions between λ-states are accepted with a probability following from the osmotic ensemble partition function. Biasing weights associated with the λ-states are used in order to efficiently realize transitions between them; these are determined by means of the Wang-Landau method. We also propose a novel scaling procedure for λ, which can be used for both nonpolarizable and polarizable models of aqueous electrolyte systems. The approach is readily extended to involve other solvents, multiple electrolytes, and species complexation reactions. The method is illustrated for NaCl, using SPC/E water and several force field models for NaCl from the literature, and the results are compared with experiment at ambient conditions. Good agreement is obtained for the chemical potential-concentration curve and the solubility prediction is reasonable. Future improvements to the predictions will require improved force field models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".