Toward accurate prediction of N2 uptake capacity in metal-organic frameworks
Bibliographic record
Abstract
The efficient and cost-effective purification of natural gas, particularly through adsorption-based processes, is critical for energy and environmental applications. This study investigates the nitrogen (N 2 ) adsorption capacity across various Metal-Organic Frameworks (MOFs) using a comprehensive dataset comprising 3246 experimental measurements. To model and predict N 2 uptake behavior, four advanced machine learning algorithms—Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), and Gaussian Process Regression with Rational Quadratic Kernel (GPR-RQ)—were developed and evaluated. These models incorporate key physicochemical parameters, including temperature, pressure, pore volume, and surface area. Among the developed models, XGBoost demonstrated superior predictive accuracy, achieving the lowest root mean square error (RMSE = 0.6085), the highest coefficient of determination (R 2 = 0.9984), and the smallest standard deviation (SD = 0.60). Model performance was rigorously validated using statistical metrics and graphical analysis. Trend consistency with experimental data confirmed that XGBoost accurately captures the effect of pressure on N₂ uptake. Additionally, SHAP (Shapley Additive Explanations) analysis identified temperature as the most influential factor in adsorption prediction. Finally, an outlier assessment using the Leverage method indicated that approximately 94% of the data points were statistically valid and within the model’s applicability domain.
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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".