Characterization of Quantum Derived Electronic Properties of Molecules: A Computational Intelligence Approach
Bibliographic record
Abstract
Abstract The availability of BIG molecular databases derived from quantum mechanics computations represent an opportunity for computational intelligence practitioners to develop new tools with same accuracy but much lower computational complexity compared to the costly Schrödinger equation. In this study, unsupervised and supervised learning methods are applied to investigate the internal structure of the data and to learn the mapping between the atomic coordinates of molecules and their properties. Low dimensional spaces revealed a well defined clustering structure as defined by the measures used for comparing molecules based their atom distributions and chemical composition. Supervised learning techniques were applied on the original predictor variables, as well as on a subset of selected variables found using evolutionary algorithms guided by residual variance analysis (Gamma Test). Black and white box modeling approaches were used (random forests, neural networks and model trees and adaptive regression respectively). All of them delivered good performance, error and correlation-wise, with neural networks producing the best results. In particular white box techniques obtained explicit functional dependencies, some of them achieving considerably reduction of the feature set and expressed as simple models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".