A molecular dynamics strategy to investigate the effect of liquid additives in liquid-assisted mechanochemistry
Bibliographic record
Abstract
We describe a computational modeling strategy to investigate the dynamics and mobility of molecules in systems that compositionally correspond to the liquid-assisted environments, which are of importance to increasingly popular mechanochemical and other solvent-limited systems in which the amount of a liquid phase is tens or hundreds of times smaller compared to conventional solution environments. This work investigates how the presence of minute quantities of intimately mixed-in small-molecule solvents can influence the rigidity and mobility of an initially crystalline molecular solid, offering a starting point towards the understanding and modelling of liquid-assisted reactivity of relevance in mechanochemistry, as well as to a wide range of aging transformations relevant to, for example, the environmental stability of molecular solids such as pharmaceuticals. In effect, this study allowed us to evaluate changes to the molecular and structural dynamics with increasing amounts of a liquid additive, providing a nanoscopic view of the liquid-assisted environment, based on the number and choice of the additive and their nature, contextualized in the macroscopic and experimentally relevant η parameter. This represents, to our knowledge, the first attempt at an in silico modelling study to understand the fundamental aspects of the liquid-assisted environments found in a wide range of mechanochemical and mechanically-activated reactions. The herein presented theoretical modelling approach should be seen as a step towards detailed, quantitative studies of how the liquid-assisted environments might compare to bulk-solution environments characteristic of traditional solvent-based chemistry.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".