Machine Learning Enables a Top-Down Approach to Mechanistic Elucidation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
General reaction behavior is rarely reported in asymmetric catalysis, not simply because it is difficult to achieve, but also due to the methods used for its identification and study. Traditional approaches involve compartmentalization, where the impact of individual components is first analyzed, followed by assimilation using simple response and structure matching techniques. However, extending this method to accommodate complex conditions and diverse reactions proves challenging. Here, we present a data-driven method that relies on clusterwise linear regression to derive and predictively apply general mechanistic models of enantioinduction, with minimal human intervention. When applied to the palladium-catalyzed decarboxylative asymmetric allylic alkylation (DAAA) reaction, unexpected interactions governing enantioselectivity are revealed, supported by high-level computations and additional experiments. Our results demonstrate this workflow as a powerful new tool for automating mechanistic elucidation and effectively identifying general reaction performance.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it