Key role of density functional approximation in predicting M-N-C catalyst activities for oxygen reduction
Bibliographic record
Abstract
Metal-Nitrogen-Carbon motifs present intriguing structural and electronic properties for a number of applications, including as oxygen reduction catalysts. However, computational investigations of M-N-C-catalyzed reactions have must grapple with their complex electronic structures. In the present study, we evaluate the impact of the density functional approximation on calculated M-N-C catalyst activities for oxygen reduction. Using metalloporphyrins as model catalysts, we find a significant split between pure (GGA) and hybrid functionals, with hybrid functionals, in particular B3LYP, showing greater agreement with DLPNO-CCSD(T) reaction energies. Notably, double-hybrids offered no noticeable improvement over the much more computationally efficient B3LYP and PBE0. Other discrepancies between functionals, including ground state spin and geometry, are also considered in this work. Finally, both hybrid and double-hybrid functionals greatly reduced the gas phase errors associated with the main group molecules in the oxygen reduction reaction relative to GGA calculations, leading us to question the application of widely used empirical corrections to O$_2$.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".