Electron Density Transport During Chemical Reactions
Bibliographic record
Abstract
Statistical approaches are an increasingly powerful technique for characterizing changes in the electronic structure during reactions or molecular excitations. High-throughput studies in complex environments, in particular, benefit from methods that are both computationally efficient and require minimal pre- or postprocessing of electronic structure outputs. To address this need, we investigate optimal transport (OT), which compares probability distributions through a cost-minimizing transport plan. By applying OT to electron densities along a reaction coordinate and partitioning the resulting transport plan, we reveal how noncore electron density evolves during chemical processes. We demonstrate the approach on two systems: Bergman cyclization and proton transfer occurring within a water cluster. Along the intrinsic reaction coordinate of Bergman cyclization, OT yields chemically intuitive insights and complements information provided by the electron localization function. For the proton-transfer reaction, based on ab initio molecular dynamics, OT clearly identifies individual transfer events. Together, these studies demonstrate that optimal transport provides a promising new framework for investigating chemical reactivity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".