Predicting hydration Gibbs energies of alkyl-aromatics using molecular simulation: a comparison of current force fields and the development of a new parameter set for accurate solvation data
Bibliographic record
Abstract
The Gibbs energy of hydration is an important quantity to understand the molecular behavior in aqueous systems at constant temperature and pressure. In this work we review the performance of some popular force fields, namely TraPPE, OPLS-AA and Gromos, in reproducing the experimental Gibbs energies of hydration of several alkyl-aromatic compounds--benzene, mono-, di- and tri-substituted alkylbenzenes--using molecular simulation techniques. In the second part of the paper, we report a new model that is able to improve such hydration energy predictions, based on Lennard Jones parameters from the recent TraPPE-EH force field and atomic partial charges obtained from natural population analysis of density functional theory calculations. We apply a scaling factor determined by fitting the experimental hydration energy of only two solutes, and then present a simple rule to generate atomic partial charges for different substituted alkyl-aromatics. This rule has the added advantages of eliminating the unnecessary assumption of fixed charge on every substituted carbon atom and providing a simple guideline for extrapolating the charge assignment to any multi-substituted alkyl-aromatic molecule. The point charges derived here yield excellent predictions of experimental Gibbs energies of hydration, with an overall absolute average deviation of less than 0.6 kJ mol(-1). This new parameter set can also give good predictive performance for other thermodynamic properties and liquid structural information.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".