Automated Predictive Chemical Reaction Modelling applied to Gold(I) Catalysis
Bibliographic record
Abstract
Computational modelling is a powerful tool to study chemical reactions. Currently, human guidance is nearly always required to avoid the untractable complexity of all a priori possible reaction steps, which consequently greatly limits automated predictive applications. Despite recent advances in the field, predictive reaction modelling without human guidance remains limited. In this work, we present a theoretical framework based on atomic reactivity as well as a "neophile" kinetic model, demonstrating how they enable unbiased automated reaction modelling with molecules of size typically encountered in experimental methodologies. Our framework allows the identification of unlikely or redundant reaction steps based on first principles and previous analyses, while the neophile kinetic model separates crucial reaction intermediates from inconsequential ones. These advances greatly improved modelling efficiency and allowed us to automatically model 17 unimolecular gold(I)-catalyzed reactions of increasing complexity starting only from the reactant and catalyst. In 11 reactions, the experimental product distribution is closely reproduced, with an additional 4 being essentially correct. Our results demonstrate that it is possible to predictively model catalytic reactions without human guidance through a convenient reformulation of the problem. We anticipate that this work will enable the rapid generation of unbiased reaction data. In addition to providing chemical insight, this data could train machine-learning models to manifest mechanism-based chemical reasoning. These models could eventually be combined with self-driving laboratories to form powerful self-teaching, self-correcting autonomous research agents.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".