Method to Predict Reagents in Iridium-Based Photoredox Catalysis
Bibliographic record
Abstract
Visible-light photoredox catalysts with oxidizing excited states have been broadly applied in organic synthesis. Following photon absorption by the photocatalyst, electron transfer from an organic reagent is the most common mechanistic outcome for this class of reaction. Reduction potentials for organic reagents are therefore useful to predict reactivity and DFT proved to be useful as a predictive tool in this regard. Due to the complex mechanisms that follow electron transfer, kinetics play a crucial role in the success of photoredox reactions. We extend the predictive tools of DFT to estimate the electron transfer <i>rates</i> between an excited photocatalyst and various organic substrates. To calibrate our model, 49 electron transfer rate constants were experimentally measured in acetonitrile for the catalyst Ir[dF(CF<sub>3</sub>)ppy]<sub>2</sub>(dtbpy)<sup>+</sup>. The rate constants, k<i><sub>q</sub></i>, gave a clear predictive trend when compared to calculated ionization energies in “frozen solvent”, which was a better predictor than standard reduction potentials in our case. The calculated k<i><sub>q</sub></i> gave an average error of 17% for log(k<sub>q</sub>) values between 4 and 11. This simple method can predict the reactivity of hundreds of reagents <i>in silico</i>. Notably, the calculations offered unexpected insight that we could translate into success for the C-H activation of acetylacetone as a proof-of-concept.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".