Transferable Learning of Reaction Pathways from Geometric Priors
Bibliographic record
Abstract
Identifying minimum-energy paths (MEPs) is a crucial application of molecular simulations to understand chemical reaction mechanisms but is computationally demanding. We introduce MEPIN, a scalable machine-learning method that predicts MEPs from reactant and product configurations, without relying on transition-state geometries or preoptimized reaction paths during training. The MEPIN task is defined as predicting the deviation between ground-truth MEPs and purely geometric interpolations along the reaction coordinates. The model predicts a continuous reaction path using a symmetry-broken equivariant neural network architecture that generates a flexible number of intermediate structures. MEPIN is trained on an energy-based objective, and we report efficiency gains of also using geometric priors from geodesic interpolation as initial interpolations or as pretraining objectives. The approach generalizes across diverse chemical reactions and achieves accurate alignment with reference intrinsic reaction coordinates, as demonstrated in various small molecule reactions and [3 + 2] cycloadditions.
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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 | 0.001 | 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 | 0.001 | 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".