Accurate Prediction of Copolymerization Statistics in Molecular Olefin Polymerization Catalysis: The Role of Entropic, Electronic, and Steric Effects in Catalyst Comonomer Affinity
Bibliographic record
Abstract
Accurate in silico prediction of copolymerization performance of olefin polymerization catalysts is demonstrated. It is shown by the example of 19 metallocene and post-metallocene group IV metal (Ti, Zr, Hf) systems that DFT (M06-2X(PCM)/TZ//TPSSTPSS/DZ) can accurately describe the copolymerization factor r e: i.e., the competition of ethene and propene for insertion in metal n -alkyl bonds. Experimental r e values were computationally reproduced with a mean average deviation (MAD) and maximum deviation of only 0.2 and 0.5 kcal/mol, respectively. Both dispersion and solvent corrections play a crucial role in achieving this accuracy. Ethene insertion is found to be entropically favored for all catalysts due to a combination of symmetry factors and less congested insertion geometries. The enthalpic preference for either ethene or propene is catalyst dependent. The predictions are based on straightforward calculation of relevant insertion transition state energies; there are no indications for a shift in rate-limiting step from insertion to e.g. olefin capture or chain rotation.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".