Quantitative Structure–Property Relationship Models for Recognizing Metal Organic Frameworks (MOFs) with High CO<sub>2</sub> Working Capacity and CO<sub>2</sub>/CH<sub>4</sub> Selectivity for Methane Purification
Bibliographic record
Abstract
Metal‐organic frameworks (MOFs) can theoretically yield a nearly infinite number of nanoporous materials, which represents a combinatorial design challenge that demands computational tools rather than experimental trial‐and‐error. Here we report Quantitative Structure–Property Relationship (QSPR) models to identify high‐performing MOFs for methane purification solely using geometrical features. The CO 2 working capacity and CO 2 /CH 4 selectivity of ca. 320,000 hypothetical MOF structures was computed at conditions relevant to natural gas purification using grand canonical Monte‐Carlo (GCMC) simulations. Using 32,500 MOF structures we calibrated binary decision tree (DT) and support vector machine (SVM) models that can accurately identify high‐performing MOFs based on their pore size, void fraction and surface area. DT models yielded guidelines of pore size, void fraction and surface area for designing high‐performing materials. The SVM machine learning classifiers could be used to quickly pre‐screen MOFs, such that compute intensive GCMC simulations are not performed on all structures. The SVM classifiers were tested on ca. 290,000 MOFs that were not part of the training set and could correctly identify up to 90 % of high‐performing MOFs while only flagging a fraction of the MOFs for more rigorous screening. QSPR models constitute efficient computational tools for the virtual screening of large structural libraries and provide rational design rules for the discovery of sorbents for methane purification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".