Robust Machine Learning Models for Predicting High CO<sub>2</sub> Working Capacity and CO<sub>2</sub>/H<sub>2</sub> Selectivity of Gas Adsorption in Metal Organic Frameworks for Precombustion Carbon Capture
Bibliographic record
Abstract
This work is devoted to the development of quantitative structure–property relationship (QSPR) models using machine learning to predict CO 2 working capacity and CO 2 /H 2 selectivity for precombustion carbon capture using a topologically diverse database of hypothetical metal–organic framework (MOF) structures (358 400 MOFs, 1166 network topologies). Such a diversity of the networks topology is much higher than previously used (<20 network topologies) for rapid and accurate recognition of high-performing MOFs for other gas-separation applications. The gradient boosted trees regression method allowed us to use 80% of the database as a training set, while the rest was used for the validation and test set. The QSPR models are first built using purely geometric descriptors of MOFs such as gravimetric surface area and void fraction. Additional models which account for chemical features of MOFs are constructed using atomic property weighted radial distribution functions (AP-RDFs) with a novel normalization to accommodate the size diversity of the MOF database. It is shown that the best models for CO 2 working capacities ( R 2 = 0.944) and CO 2 /H 2 selectivities ( R 2 = 0.872) are built from a combination of six geometric descriptors and three AP-RDF descriptors. However, more important is that our QSPR models can identify top 1000 high-performing MOFs in just top 3000 or 5000 MOFs. This work shows that QSPR modeling can account for the topological diversity of MOFs and accelerate the screening for top-performing MOFs for precombustion carbon capture.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".