(Coulomb) Local potential energy density–supramolecular energy (LPED–SME) machine learning prediction—a web application to obtain the local SME from simple inputs
Bibliographic record
Abstract
We developed a freely accessible web application ( https://clfirme.pythonanywhere.com/ ) that uses supervised machine learning (ML) to predict (Coulomb) local potential energy density (LPED) for intermolecular and intramolecular interactions. The user-friendly interface accepts simple inputs: atomic charges of interacting atoms and interatomic distances, avoiding the complex calculations typically required by Quantum Theory of Atoms in Molecules topology. Our learning curves demonstrate model stability relative to dataset size, leveraging uncoded, smooth features with strong physical relationships to the target LPED. After testing six different ML models, we found that Regularized Gradient Boosting performed best, achieving excellent predictive capacity in both primary and secondary testing. This model has an average uncertainty estimate of 40.9%, with larger errors observed for highly negative LPED values.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".