<scp>ML</scp> ‐driven models for predicting <scp> CO <sub>2</sub> </scp> uptake in metal–organic frameworks ( <scp>MOFs</scp> )
Bibliographic record
Abstract
Abstract This study advances the discourse on the application of machine learning (ML) algorithms for the predictive analysis of CO 2 uptake in metal–organic frameworks (MOFs), with a nuanced focus on the CATBoost model's capability to navigate the complexities inherent in MOFs' heterogeneous landscape. Building upon and extending the comparative analysis, our investigation underscores the CATBoost model's remarkable prediction robustness, characterized by a significant reduction in root mean square error (RMSE) and an enhanced R‐squared (R 2 ) value, thereby affirming its superior accuracy and reliability in forecasting CO 2 adsorption. A pivotal aspect of our research is the integration of SHapley Additive exPlanations (SHAP) values for a detailed assessment of feature importance, which not only corroborated ‘pressure’ and ‘surface area’ as pivotal determinants of CO 2 uptake but also illuminated the model's advanced analytical capabilities in handling categorical features and mitigating overfitting, even within a dataset marked by intricate and non‐linear patterns. Our quantitative and conceptual analysis, showcasing up to a 15% improvement in RMSE over previous models, reveals the CATBoost model's unparalleled efficiency in discerning the multifaceted interplay of factors influencing CO 2 adsorption. This is crucial for the strategic engineering of MOFs with optimized properties. Beyond ‘pressure’ and ‘surface area’, our SHAP analysis highlighted other descriptors with substantial values, elucidating their contributions to CO 2 uptake and providing invaluable insights for the MOF design process.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".