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 rates 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 3 )ppy] 2 (dtbpy) + . The rate constants, k q , 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 q gave an average error of 17% for log(k q ) values between 4 and 11. This simple method can predict the reactivity of hundreds of reagents in silico . Notably, the calculations offered unexpected insight that we could translate into success for the C-H activation of acetylacetone as a proof-of-concept.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".