Bibliographic record
Abstract
Although proton-transfer reactions are ubiquitous in chemistry and biology, their theoretical study is limited by number of difficulties. Some of the practical challenges involving the study of proton-transfer reactions include the fact that accurate descriptions of hydrogen-bonded systems require time-consuming ab initio electronic structure methods. Computer time limitations become particularly relevant when investigating “rare events” such as chemical reactions, especially when the reactions are accompanied by substantial differences in the structure of the solvent. In addition, the proton is the lightest nucleus and nuclear quantum effects must be described adequately in proton-transfer reactions. The problems mentioned above are addressed by proposing methodological improvements to calculate reaction rates for proton-transfer reactions. A new ab initio Monte Carlo algorithm is proposed in which a much faster molecular mechanics potential “guides” the slow ab initio electronic structure simulation resulting in significantly more efficient sampling schemes compared to the more familiar ab initio molecular dynamics methods. The molecular mechanics guided sampling method is generalized to incorporate nuclear quantum effects via centroid transition state theory and solvent effects via hybrid quantum mechanics-molecular mechanics methods. The use of the molecular mechanics based importance function method is exemplified in a study of model intra-molecular proton-transfer reactions which investigates how molecular mechanics potentials should be created in order to predict kinetic isotope effects and reaction mechanisms correctly, and to understand the conditions in which atoms other than the hydrogen atom should be treated quantum-mechanically.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".