Extensive data analysis and modelling of carbon dioxide solubility in ionic liquids using chemical structure-based ensemble learning approaches
Bibliographic record
Abstract
The significant rise in carbon dioxide (CO 2 ) emission due to industrial growth is a major global challenge. As a result, there is a need to implement various techniques to reduce and regulate this phenomenon. One such technique involves the utilization of ionic liquids (ILs) as solvents in CO 2 capturing and separation processes, which is already commonly practiced. In this study four advanced intelligent models, Extreme Gradient Boosting (XGBoost), Gradient Boosting (GBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) have been proposed to predict the solubility of CO 2 in 160 different ILs based on factors such as temperature, pressure, and the chemical structure of the ILs. Findings indicate that the XGBoost model is the most accurate among the four models, with the root mean square error (RMSE) and coefficient of determination (R 2 ) values of 0.014 and 0.9967, respectively. Moreover, the results reveal that increasing pressure, decreasing temperature, and lengthening the alkyl chain all increase the solubility of CO 2 in ILs. Furthermore, pressure and the number of –CH 2 substructure in ILs have the most significant impact on the CO 2 solubility in ILs, respectively. To ensure the XGBoost model's reliability, the model's data has been assessed using the leverage technique. The results show that 92.44 % of the data fell within the valid area, which is a substantial percentage and indicates the model's high reliability. The findings of this study will assist in designing and fine-tuning the chemical structure of ionic liquids specifically for CO 2 capture purposes.
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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 | 0.002 |
| 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 | 0.000 |
| 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".