Curing difficult cases in magnetic properties prediction with self-interaction corrected density functional theory
Bibliographic record
Abstract
The Perdew–Zunger self-interaction correction (SIC) was implemented self-consistently within a molecular density functional theory (DFT) program, using the Krieger–Li–Iafrate approximation to the optimized effective potential, and the Vosko–Wilk–Nusair (VWN) functional. The computationally efficient implementation relies on the fitting of orbital densities for the evaluation of orbital Coulomb potentials, and allows for routine applications to large molecules. Due to the use of the effective potential approach, the evaluation of the energy derivatives can be handled by standard Kohn–Sham DFT techniques in a straightforward way. The SIC-VWN technique is applied to the calculation of nuclear magnetic resonance (NMR) parameters in representative small molecules, containing C, H, N, O, and F. Removal of self-interaction leads to a substantial improvement in the calculated isotropic chemical shifts for N, O, and F, where SIC-VWN holds an advantage over both local (VWN), and gradient-corrected functionals (Becke 88-Perdew 86, BP86). For C and H isotropic chemical shifts, which are well described by the gradient-corrected functionals, SIC-VWN performs as well as BP86. SIC-VWN also improves the description of the absolute chemical shielding, and of the principal components of the NMR shielding tensors. The changes arise mainly from adjustments in the Kohn–Sham orbital energies, leading to a better description of the paramagnetic contribution to the shielding tensor. For spin–spin coupling constants, SIC-VWN improves the description of the paramagnetic contribution. At the same time, the magnitude of the Fermi contact term is underestimated, yielding mixed overall results. Slow convergence of the spin-spin coupling results with the basis set size prevents a conclusive statistical evaluation for this property. The clear physical origin of the SIC-VWN effect in the prediction of magnetic properties opens the tantalizing possibility that this technique may be effective in solving problems often encountered in the calculations of NMR parameters of heavier nuclei.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.005 | 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".