Assessment of Several DFT Functionals in Calculation of the Reduction Potentials for Ni–, Pd–, and Pt–Bis-ethylene-1,2-dithiolene and -Diselenolene Complexes
Bibliographic record
Abstract
We performed an assessment of 10 common DFT functionals to determine their suitability for calculating the reduction potentials of the ([M(S2C2H2)2](0)/[M(S2C2H2)2](1-)), ([M(Se2C2H2)2](0)/[M(Se2C2H2)2](1-)), ([M(S2C2H2)2](1-)/[M(S2C2H2)2](2-)), and ([M(Se2C2H2)2](1-)/[M(Se2C2H2)2](2-)) redox couples (M = Ni, Pd, and Pt). Overall it was found that the M06 functional leads to the best agreement with the gold standard CCSD(T) method with an average difference of only +0.07 V and a RMS of 0.07 V in calculated reduction potentials. The variability in calculated reduction potentials between the various DFT functionals arise, in part, from the multireference character of these systems, which was determined by the T1 diagnostic values. Thus, the bisdiselenolene complexes show similar multireference character as the bisdithiolene complexes, which were previously shown to have such character. In particular, for the Ni-, Pd-, and Pt-bisdiselenolene complexes the average T1 values are 0.05, 0.03, and 0.02, respectively. For the CCSD(T) calculations the similarities in the reduction potentials between analogous bisdithiolene and bisdiselenolene redox couples, which appear to be independent of the metal, is a result of the noninnocence of the dithiolene and diselenolene ligands. Thus, the reduction potential is more dependent on the ligand than the metal.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".