Optimizing the Structures of Minimum and Transition State on the Free Energy Surface
Bibliographic record
Abstract
Presented here is the application of a scheme for optimizing the structures of minima and transition states on the free energy surface (FES) for a path along a fixed reaction coordinate with the aid of ab initio molecular dynamics (AIMD) simulation. In the direction of the reaction coordinate, the values corresponding to the stationary points were optimized using the quasi-Newton method, in which the gradient of the free energy along the reaction coordinate was obtained by a constraint AIMD method, and the Bofill Hessian update scheme was used. The equilibrium values for the other directions were taken as the corresponding averages in the dynamic simulation. This scheme was applied to several elementary bimolecular addition reactions: (A) BH(3) + H(2)O --> H(2)O.BH(3); (B) BF(3) + NH(3) --> FB(3).NH(3); (C) SO(3) + NH(3) --> O(3)S.NH(3); (D) C(2)H(4) + CCl(2) --> H(4)C(2).CCl(2); (E) Ni(NH(2))(2) + PH(3) --> (NH(2))(2)Ni.PH(3); (F) W(CO)(5) + CO --> W(CO)(6). For reactions A, B, C, and F, no transition state (TS) exists on the potential energy surface (PES). However there is a TS on the FES. This stems from the curvature difference of the PES and -TDeltaS as a function of the reaction coordinate. For all reactions, it is found that the TS shifts toward the complexation product with increasing temperature because of the curvature increase of -TDeltaS. The equilibrium bond distances for the inactive coordinates perpendicular to the reaction coordinate always increase with temperature, which is due to the thermal excitation and anharmonicity of the PES.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".