Large-Scale Quantitative Structure–Property Relationship (QSPR) Analysis of Methane Storage in Metal–Organic Frameworks
Bibliographic record
Abstract
Metal–organic frameworks (MOFs) present a combinatorial design challenge. The structural building blocks of MOFs can be combined to synthesize a nearly infinite number of materials. This suggests that computational tools, rather than experimental trial and error, can be used for high-throughput screening. Here, in the context of methane storage, we report the first large-scale, quantitative structure–property relationship (QSPR) analysis of MOFs. We investigated the effect of geometrical features, such as pore size and void fraction, on the simulated methane storage capacities of ∼130 000 hypothetical MOFs at 1, 35, and 100 bar at 298 K. From these data we developed models that can predict methane storage with high accuracy, based only on knowledge of the geometric features. Several models were developed: multilinear regression (MLR) models, decision trees (DTs), and nonlinear support vector machines (SVMs). In each case, 10 000 MOF structures were used to “train” the QSPR regression models, and the accuracy of the predictions was evaluated on a test set of ∼120 000 MOFs. The nonlinear SVM models can predict the methane storage capacity of MOFs in the test set with R 2 values of 0.82 and 0.93 at 35 and 100 bar, respectively. Decision tree models produced rules for optimal design: for methane storage at 35 bar, MOFs should have densities greater than 0.43 g/cm 3 and void fractions greater than 0.52; for methane storage at 100 bar, MOFs should have densities greater than 0.33 g/cm 3 and void fractions greater than 0.62. Using two-dimensional response-surface analyses of the SVM models, we developed new hypotheses about combinations of material properties, yet unexplored, that might lead to very high methane storage capacities and warrant further investigation. SVM-based predictions of methane storage from MOF structural features can be tested online at our Web site: http://titan.chem.uottawa.ca/woolab/MOFIA .
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".