Reevaluating the Stability and Prevalence of Conglomerates: Implications for Preferential Crystallization
Bibliographic record
Abstract
Chiral resolution by preferential crystallization from a racemic or scalemic solution occurs by selective crystallization of a single enantiomer as a homochiral solid phase, known as a conglomerate. However, there is a prevailing perception that stable homochiral crystals are quite rare and are estimated to form in only 5–10% of all chiral compounds. In this work, the prevalence rate of stable conglomerates is reexamined using dispersion-corrected density-functional theory calculations for a collection of homochiral and heterochiral crystal pairs. The homochiral crystal is found to be the thermodynamically stable phase for 19% of the examined compounds. This value represents a lower bound of the prevalence rate since our sample is necessarily biased because the comparison is limited to cases where a stable heterochiral phase exists and does not include molecules with no reported heterochiral phase. Even so, this lower bound is two to four times higher than the often-quoted conglomerate prevalence rate, a value that is also based on (experimental) thermodynamic quantities. In addition, our results are used to reexamine Wallach’s rule and the close-packing principle. It is concluded that the prevalence of stable conglomerates has been underestimated, and, provided thermodynamic equilibrium drives the crystallization process, preferential crystallization has a much wider scope of applicability than previously assumed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".