A molecular dynamics study of chirality transfer: The impact of a chiral solute on an achiral solvent
Bibliographic record
Abstract
A solvation shell may adapt to the presence of a chiral solute by becoming chiral. The extent of this chirality transfer and its dependence on the solute and solvent characteristics are explored in this article. Molecular dynamics simulations of solvated chiral analytes form the basis of the analysis. The chirality induced in the solvent is assessed based on a series of related chirality indexes originally proposed by Osipov [M. A. Osipov et al., Mol. Phys. 84, 1193 (1995)]. Two solvents are considered: Ethanol and benzyl alcohol. Ethanol provides insight into chirality transfer when the solvent interacts with the solute primarily by a hydrogen bond. Several ethanol models have been considered starting with a nonpolarizable model, progressing to a fluctuating charge model, and finally, to a fully polarizable model. This progression provides some insights into the importance of solvent polarizability in the transfer of chirality. Benzyl alcohol, by virtue of the aromatic ring, increases the number of potential solvent-solute interactions. Thus, with these two solvents, the issue of compatibility between the solvent and solute is also considered. The solvation of three chiral solutes is examined: Styrene oxide, acenaphthenol, and n-(1-(4-bromophenyl)ethyl)pivalamide (PAMD). All three solutes have the possibility of hydrogen bonding with the solvent, the last two may also form ring-ring interactions, and the last also has multiple hydrogen bonding sites. For PAMD, the impact of conformational averaging is examined by comparing the chirality transfer about rigid and flexible solutes.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".