Hildebrand-Assessed Margules (HAM) interaction parameter: applications to surfactant and polar oil partition
Bibliographic record
Abstract
The Hildebrand-Assessed Margules (HAM) method uses Henry's law constant and vapor pressure for pure components from free cheminformatic software to obtain the interaction parameter of a component "i" with water (Aiw) and its molar volume (Vmi). Inspired by the concept of the solubility parameter of Hildebrand (δi), where (Aiw/Vmi)^0.5 = (δi-δw), HAM makes a simple assessment of the binary interaction parameter Aij: (δi-δj) =(Aij/Vmi)^0.5 = (δi-δw)-(δi-δw)= (Aiw/Vmi)^0.5- (Ajw/Vmi)^0.5. The Aij predictions from this relatively simple expression were compared to literature Aij data obtained from activity coefficients of "i" at infinite dilution in a solvent "j". The performance of the HAM framework was compared to other predictive models, UNIFAC, MOSCED, COSMO-RS, HSP and the original Hildebrand model. The HAM method overpredicted Aij (>1Aij unit) in systems where the solvent was an acid or a base that could dissociate in the presence of water. However, for most systems (small polar molecules, short chain alcohols, medium chain alcohols, aromatics and alkanes), the Aij values were predicted within 1 Aij unit and commonly within 0.5 Aij units. A systemic underprediction of Aij was observed when the HAM-predicted log P was compared to predictions from the ACD/Labs software, which required the introduction of a correction term. The corrected HAM method reproduced the partition coefficient of surfactant and polar molecules with an RMSE of 1.05 and an NRMSE of 15%, comparable to other models that require more inputs and resources.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".