Machine learning for data-driven insights into CO2 adsorption on amorphous porous organic polymers
Bibliographic record
Abstract
Increasing CO 2 emissions demand advanced carbon capture and storage technologies. Among various approaches, the adsorption of CO 2 on porous organic polymers (POPs) is particularly promising due to their low density, high surface area, tunable pore structure, and excellent thermal and chemical stability. However, optimizing CO 2 uptake is challenging because the quantitative relationships between material properties and adsorption capacity remain unclear. Although machine learning (ML) algorithms have improved predictive performance, many models offer limited actionable insights for material design due to poor interpretability. In this study, we apply four supervised ML models random forest, light gradient boosting, extreme gradient boosting, and support vector machines to predict the CO 2 adsorption capacity of amorphous POPs using a comprehensive dataset (8 inputs and 737 data points) that integrates textural properties, elemental composition, and operating conditions. The extreme gradient boosting model achieved the best performance (R 2 = 0.995; RMSE = 0.056; MAE = 0.0321). Beyond prediction, we employ SHapley Additive exPlanations, permutation importance, and uni‑factorial partial dependence analysis to quantitatively elucidate the role of individual descriptors. Our results reveal that operating conditions and textural features (e.g., BET surface area and micropore volume) exert a greater influence on CO 2 uptake than elemental composition. These data-driven insights provide a roadmap for the rational design of next-generation POP adsorbents for efficient carbon capture.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".