Aromaticity of rings-in-molecules (RIMs) from electron localization–delocalization matrices (LDMs)
Bibliographic record
Abstract
A new and powerful molecular descriptor termed the LDM (localization–delocalization matrix) has recently been proposed as a molecular fingerprinting tool and has been shown to yield robust quantitative-structure-to-activity/property-relationships (QSAR/QSPR). An LDM lists the average number of electrons localized within an atom in a molecule along its diagonal while the off-diagonal elements are the pair-wise average number of electrons shared between every pair of atoms in the molecule, bonded or not. Hence, the LDM is a representation of a fuzzy molecular graph that accounts for the whereabouts of all electron(s) in the molecule and can be expected to encode for several facets of its chemistry at once. We show that the LDM captures the aromatic character of a ring-in-a-molecule by comparing the aromaticity ranking based on the LDMs and their eigenvalues of 6-membered carbon rings within (polycyclic) benzenoid hydrocarbons with the ranking based on four well-established local aromaticity measures (harmonic oscillator model of aromaticity, acromatic fluctuation index, para delocalization index, and nucleus independent chemical shift(0)).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".