Chemical applications of electron localization-delocalization matrices (LDMs) with an emphasis on predicting molecular properties
Bibliographic record
Abstract
A matrix is constructed where the vertices (atoms) are connected by edges (bonds) resulting in a square matrix that is symmetrical.The localization index (unshared electrons) occupies the long diagonal where the delocalization index (shared electrons between two different atoms divided by 2) represent the off-diagonal elements.Such a matrix is called a localization-delocalization matrix or LDM.These matrices have shown promise as a novel Quantitative Structure Activity Relationship (QSAR) method via the Frobenius Distance, a method to compare matrices of similar sizes that returns a Euclidean distance.Some notable results that will be expanded upon are that for a series of 14 para-substituted benzoic acids for pKa prediction (r 2 = 0.986), and a series of 13 polycyclic benzenoid hydrocarbons (PBH) separated by inner and outer rings (r 2 = 0.97).A program (AIMLDM) was developed in Python 3.4.1 to construct these matrices and perform the required calculations.September 12, 2016 i Frobenius distance dissimilarity to benzene. . . . . . . . . . . . . . .4.3 Pairwise vector angles (in degrees ( • )) matrix for the ring in molecules to three decimals . . . . . . . . . . . . . .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".