Design of [2]rotaxane through image threshold segmentation of electrostatic potential image
Bibliographic record
Abstract
An electrostatic potential (ESP)-based image segmentation method has been used to estimate the ability of proton donation and acceptance involved in ring-rod recognition. The relative binding strength of [2]rotaxane has also been further estimated from the difference of the characteristic image-segmentation derived ESP between proton donor and proton acceptor. The size and electrostatic compatibility criteria are introduced to guide the design of interlocked [2]rotaxane. A library of 75 thermodynamically stable [2]rotaxane candidates has been generated, including 16 experimentally known systems. The theoretical results for 16 experimentally known [2]rotaxanes are in good agreement with both the experimental association constants and density functional theory-calculated binding energies. Our ESP-based image segmentation model is also applicable to the tristable [2]rotaxane molecular shuttle as well as [1]rotaxane with self-inclusion function, indicating this simple method is generic in the field of constructing other supramolecular architectures formed with donor/acceptor molecular recognition. © 2016 Wiley Periodicals, Inc.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".