Revealing Electron–Electron Interactions within Lewis Pairs in Chemical Systems
Bibliographic record
Abstract
The so-called "Lewis pair" is a ubiquitous phenomenon in chemistry and is often used as an intuitive construct to predict and rationalize chemical structure and behavior. Concepts from the very general Valence Shell Electron Pair Repulsion (VSEPR) model to the most esoteric reaction mechanism routinely rely on the notion that electrons tend to exist in pairs and that these pairs can be thought of as being localized to a particular region of space. It is precisely this localization that allows one to intuit how these pairs might behave, generally speaking, so that reasonable predictions may be made regarding molecular structure, intermolecular interactions, property trends, and reaction mechanisms, etc. Of course, it is rather unfortunate that the Lewis model is entirely qualitative and yields no information regarding how any specific electron pair is distributed. Here we demonstrate a novel electronic structure analysis technique that predicts and analyzes precise quantitative details about the relative and absolute distribution of individual electron pairs. This Single Electron Pair Distribution Analysis (SEPDA) reveals quantitative details about the distribution of the well-known Lewis pairs, such as how they are distributed in space and how their relative velocities change in various chemical contexts. We show that these distributions allow one to image the explicitly pairwise electronic behavior of bonds and lone pairs. We further demonstrate how this electronic behavior changes with several conditions to explore the nature of the covalent chemical bond, non-covalent interactions, bond formation, and exotic 3-center-2-electron species. It is shown that indications of the strength of bonded and non-bonded interactions may also be gleaned from such distributions and SEPDA can be used as a tool to differentiate between interaction types. We anticipate that SEPDA will be of broad utility in a wide variety of chemical contexts because it affords a very detailed, visual and intuitive analysis technique that is generally applicable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".