Range-Separated DFT Functionals are Necessary to Model Thio-Michael Additions
Bibliographic record
Abstract
The textbook mechanism for the addition of a thiol to an olefin is the Michael-type addition, which involves a nucleophilic attack of a thiolate anion on an alkene to form a carbanion intermediate. Previous computational models of these reactions have proposed alternative mechanisms, as no minimum corresponding to the carbanion intermediate was present on the potential energy surface. We show that many popular pure and hybrid DFT functionals, such as PBE and B3LYP, erroneously predict that the carbanion is not an intermediate, favoring a noncovalent charge-transfer complex stabilized spuriously by delocalization error. Range-separated DFT functionals correct this problem and predict stable carbanion structures and energies. In particular, calculations using the ωB97X-D functional are in close agreement with CCSD(T) data for the structures and energies of a series of thio-carbanions. Range-separated functionals will make it possible to model the reaction mechanisms of Michael-type additions that occur in biochemistry, such as the covalent modification of a cysteine side chain by drugs containing an electrophilic double bond.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".