Molecular Force Field Development for Aqueous Electrolytes: 1. Incorporating Appropriate Experimental Data and the Inadequacy of Simple Electrolyte Force Fields Based on Lennard-Jones and Point Charge Interactions with Lorentz–Berthelot Rules
Bibliographic record
Abstract
It is known that none of the available simple molecular interaction models of aqueous electrolytes based on SPC/E water and their associated force fields are able to reproduce the concentration dependence of important thermodynamic properties of even the simplest electrolyte, NaCl, at ambient conditions over the entire experimentally accessible concentration range [ Mouc̆ka , F. ; Nezbeda , I. ; Smith , W. R. J. Chem. Phys. 2013 , 138 , 154102 ]. This paper explores the possibility of improving their performance by incorporating concentration-dependent experimental data for the total ionic chemical potential and the density into the fitting procedure, in addition to experimental values of solubility and solid chemical potential. We describe a general parameter estimation methodology for a studied class of models that incorporates the aforementioned experimental data. When the entire concentration range is considered, although the resulting force field is a slight improvement over others currently available in the literature, overall quantitative agreement with the experimental data over this range remains unsatisfactory. This indicates an inherent limitation of such simple molecular interaction models and strongly suggests that more complex mathematical forms of such models are required to quantitatively predict the properties of aqueous electrolyte solutions when the entire concentration range is of interest. Our parameter estimation methodology is also applicable to such cases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".