Improving “difficult” reaction barriers with self-interaction corrected density functional theory
Bibliographic record
Abstract
We examined 11 difficult reactions with self-interaction corrected density (SIC) functional theory. The data set includes dissociation of radicals into symmetric fragments (H2+→H+H+, He2+→He+He+), radical hydrogen abstraction (H+H2→H2+H, H+HCl→H2+Cl, H+N2H2→N2H+H2, CH3+H2→CH4+H), proton transfer [HC(OH)CHC(O)H→HC(O)CHC(OH)H], SN2 halogen exchange (X−+CH3X→CH3X+X−, X=F,Cl,Br), and closed-shell unimolecular dissociation of tetrasine (C2N4H2→N2+2HCN). Calculated self-interaction energies cancel, almost identically, for the reaction energies (ΔER), so that SIC functionals do not lead to a systematic improvement in ΔER. Self-interaction correction increases for reaction transition structures, leading to higher calculated activation barriers (ΔE≠). The average absolute deviation in ΔE≠, from ab initio and experimental barriers, is reduced from 14 kcal/mol for Vosko–Wilk–Nusair (VWN) or 12 kcal/mol for revised Perdew–Burke–Ernzerhof (revPBE) functionals to 5.4 (SIC-VWN) or 3.4 (SIC-revPBE) kcal/mol. Reorganization of the electron density, due to removal of self-interaction, appears to be important. When SIC is included as a perturbation, using self-consistent densities of the parent functional, the average absolute deviations for the barriers increase to 7.5 (VWN+SIC) or 5.3 (revPBE+SIC) kcal/mol. Gradient-corrected functionals (revPBE, BP86) reduce the magnitude of the total self-interaction correction, by improving the description of the core orbitals. For the valence orbitals, both the magnitudes of the self-interaction corrections, and their change between reagents and transition structures, are similar for VWN local density approximation, and generalized gradient approximation functionals. Reducing the magnitude of the self-interaction energy for valence electrons thus appears to be a promising direction for the development of chemically accurate exchange-correlation functionals.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".