Are the Hirshfeld and Mulliken population analysis schemes consistent with chemical intuition?
Bibliographic record
Abstract
Abstract In the present article, we report a comparative study between the Hirshfeld and Mulliken population analysis schemes (abbreviated as HPA and MPA, respectively). Trends of atomic charges derived from these two population analysis schemes are compared with those expected from other commonly used chemical concepts like electronegativity, inductive effects, and resonance effects. Although previous studies on intramolecular reactivity sequences demonstrated that HPA generates reliable and non‐negative (and thus physically more realistic) condensed Fukui function (FF) values, the present study reveals problems with the HPA charge partitioning technique. Specifically, HPA fails to reproduce reliable intermolecular and intramolecular charge trends in several systems. Reasons for the success and failure of HPA are discussed and a method for improving the Hirshfeld charge partitioning is proposed. © 2008 Wiley Periodicals, Inc. Int J Quantum Chem, 2009
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".