Efficient Machine-Learning-Based New Tools to Design Eutectic Mixtures and Predict Their Viscosity
Bibliographic record
Abstract
The development of models that accurately predict the formation of eutectic mixtures (EMs, including the well-known deep eutectic solvents) and their viscosity is imperative to save time in synthesizing new solvents. We developed reliable machine-learning-based classifiers able to discern between eutectic and noneutectic (non-EM) mixtures and regressors able to predict the viscosity of an EM. A new experimental data set of 219 EMs, 384 non-EMs, and 1450 viscosity points at different temperatures and water contents is provided and used to challenge several models, defined both by an algorithm and by descriptors. The top-performing EM/non-EM classifier yields an accuracy of 92%, and the best regressor achieves viscosity predictions with a mean absolute error of 2.2 mPa·s; the extrapolation capabilities of the latter were assessed on additional measurements at temperatures and water contents outside the range of the training data set, revealing good accuracy at low viscosities. The SHapley Additive exPlanations (SHAP) algorithm was employed in several models as an eXplainable Artificial Intelligence (XAI) technique to quantify input feature contributions to the model output. These results represent a significant step forward in developing robust and highly accurate models for determining eutectic mixtures and their viscosity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".