First-Principle Molecular Dynamic Simulations along the Intrinsic Reaction Paths
Bibliographic record
Abstract
Presented is an algorithm for performing the ab initio molecular dynamic (MD) simulations along the predetermined intrinsic reaction paths (IRP). The proposed approach has been implemented within the projector-augmented-wave DFT methodology (PAW program). The slow-growth MD simulations along the IRP has been applied for the reactions of (i) the HCN → CNH isomerization reaction, (ii) the conrotatory ring opening of cyclobutene, (iii) the prototype SN2 reaction Cl - + CH 3 Cl → ClCH 3 + Cl -, and (iv) the chloropropene isomerization Cl−CH 2 −CH CH 2 → CH 2 CH−CH 2 Cl. The results show that the slow-growth MD approach along the predetermined IRP leads to smooth free-energy profiles; use of a well-defined reaction coordinate (RC) reduces the problem of the free-energy hysteresis. Thus, the slow-growth simulations along the IRP typically require less time steps than the standard approach with an a priori chosen RC. The illustrative examples show that the applied methodology works well for the reactions involving concerted changes in many geometrical variables as well as in the cases when the finite-temperature paths strongly deviate from the IRP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".