MOF-Based CO2 Adsorption Predictions: Role of Membership and Kernel Functions in Machine Learning Models
Bibliographic record
Abstract
Summary The rising concentration of atmospheric CO2 has intensified global warming and climate change, highlighting the need to develop effective mitigation strategies. Among various approaches, adsorption using metal-organic frameworks (MOFs) has gained attention as a promising method for CO2 capture. This study investigates the predictive capabilities of machine learning (ML) models, specifically the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Coupled Simulated Annealing–Least Squares Support Vector Machine (CSA-LSSVM), in estimating CO2 adsorption by MOFs. The ANFIS model is analyzed based on membership function type, epoch number, and optimization method, while CSA-LSSVM is examined using various kernel functions to enhance predictive accuracy and minimize error. The results demonstrate that CSA-LSSVM with the Gaussian kernel function achieves the highest accuracy, outperforming other models with a training RMSE of 0.026 and R2 of 0.974.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".