Machine learning for analysing ab initio molecular dynamics simulations
Bibliographic record
Abstract
Abstract Post-calculation analyses are often required to extract physical insights from ab initio molecular dynamics simulations. In the present work, we use different machine learning classifiers to take a new perspective on the decomposition reaction of dioxetane. Upon thermally activated decomposition, dioxetane can form products in an electronically excited state and can thus chemiluminesce. Simulated dynamics trajectories exhibit both successful and frustrated dissociations. As an exhaustive and systematic study of the decomposition mechanism “by hand” is beyond feasibility, machine learning models have been employed to study the relevant nuclear distortions governing molecular dissociation. According to all classifiers used in the study, the two sets of geometries differ by the in-phase planarisation of the two formaldehyde moieties. New insights are obtained from this analysis: if both moieties are not planar enough when the dissociation is attempted, it is frustrated and the molecule remains trapped. The postponing of the decomposition reaction by the so-called entropic trap enhances the chemiexcitation efficiency.
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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".