Quantitative Reactivity Models for Oxidative Addition to L2Pd(0): Additional Substrate Classes, Solvents, and Mechanistic Insights
Bibliographic record
Abstract
Quantitative molecular structure-reactivity models are useful for generating predictions to guide synthesis design, and in formulating and testing mechanistic hypotheses. We report an expanded multivariate linear regression (MLR) model for the rate of (hetero)aryl (pseudo)halide oxidative addition to L2Pd(0), here exemplified by Pd(PCy3)2. This builds on a prior model from our group, with additional substrate classes (aryl chlorides and iodides) and reaction solvents (THF, toluene, THF/DMF mixture). Overall solvent effects across the entire substrate set are minimal under these conditions, enabling a unified MLR model without introduction of new molecular descriptors beyond the original five. Examining the mechanistic origin of the two molecular electrostatic potential (ESP) descriptors led to generation of a simpler, four descriptor model that is suitable for aryl halides, but not for 2-halopyridines. Using this model we identified a mechanistic outlier, 2-pyridyl triflate, which undergoes a nucleophilic displacement oxidative addition that does not involve the adjacent nitrogen atom. Finally, we discuss the relationship between C–X bond strength and oxidative addition rates, and compare the intrinsic bond strength index (IBSI) to bond dissociation enthalpy (BDE) as a bond strength descriptor.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".