Koopmans’ multiconfigurational self-consistent field (MCSCF) Fukui functions and MCSCF perturbation theory
Bibliographic record
Abstract
Prediction of chemical reactivity has become one of the highest priority tasks of computational chemistry since the development of the methods of modeling electronic structure. Despite the general simplicity of the physical concept of reactivity and the rapid development of modern density functional theory (DFT) methods, this task remains state-of-the-art for systems with wavefunctions that have a multiconfigurational character. In such cases, for the accurate description of reactivity one needs to use multiconfigurational approaches that are much heavier computationally then ordinary single-determinant DFT methods. Moreover, the complexity of the calculation of reactivity is increased by the necessity to calculate ionic and transition states. These computational challenges can be addressed by employing the concepts of Koopmans’ theorem and its extension to a multiconfigurational case. We present a simplified methodology for the calculation of Fukui functions, based on Koopmans’ approximation for multiconfigurational Green’s functions developed in our previous works. Also, an extension of this methodology based on perturbation theory has been developed to improve accuracy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 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".