Multiproperty Deep Learning of the Correlation Energy of Electrons and the Physicochemical Properties of Molecules
Why this work is in the frame
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Bibliographic record
Abstract
The density-based descriptors from the information-theoretic approach (ITA) are used as features for multiproperty deep learning (DL), predicting the correlation energy and physicochemical properties of molecules. In addition to response properties (molecular polarizability α iso and NMR shielding constant σ iso ) where ITA has been shown to work well before, we consider four conceptually distinct but practically related concepts: electron correlation, redox potential, octanol–water partition coefficient (log K ow ), and the wavelength of maximum absorption (λ max ). The DL-predicted results are in good agreement with either the calculated or experimental counterparts, indicative of the model’s robustness. We verified the transferability of redox potentials of phenazine derivatives. Generalizability is observed for the λ max data: small chromophores are used for training/validation but the test set has sizable molecules. The trained DL model outperforms the conventional TD-DFT method in terms of accuracy and efficiency. We also showcase that the isotropic quadrupole moment (Θ iso ) is a good predictor of log K ow . This establishes that versatile density-based ITA quantities can be used to make accurate, low-cost predictions of both extensive and intensive properties, suggesting that this ITA-DL protocol has the potential for closed-loop chemistry automation. Implication of this work is straightforward, that a universal framework should be possible based on the ITA-based DL models.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it