(Coulomb) LPED-SME Machine Learning Prediction
Bibliographic record
Abstract
LPED-SME Machine Learning Prediction is a Flask app to predict the local potential energy density (LPED) and supramolecular energy (SME) of a molecular complex with single or multiple intermolecular interactions, The ML model uses 66-sample dataset obtained from our previous works on LPED and from a paper to be published soon in Canadian Journal of Chemistry (2025). We have tested several machine learning models and the best one with the best metrics was the Regularized Gradient Boosting (RGG). The RGB provides a multi-factor linear relationship whose coefficient of determination (R2) is 0.86, MAE = 1.06 kcal mol-1 Bohr-3, and RMSE is 1.19 kcal mol-1 Bohr-3. The LPED equation is based on Coulomb law and it uses topological data from the Quantum Theory of Atoms in Molecules (QTAIM) and it has an excellent correlation with SME. The user must provide three simple informations: the interatomic distance between the interacting atoms of the complex and the corresponding MK, Chelpg or RESP atomic charges of the corresponding atoms of the intermolecular interaction. It is possible to input this set of information for a single interaction or a csv file with the corresponding inputs for multiple interactions.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.073 | 0.012 |
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; both teacher heads agree on what is shown here.
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".