Molecular simulations and deep neural networks‐based interpretable machine learning modelling of reverse adsorptive <scp>MOFs</scp> for ethane/ethylene separation
Bibliographic record
Abstract
Abstract The thermal decomposition of ethane (C 2 H 6 ) and the steam cracking of fossil fuels are the main sources of ethylene (C 2 H 4 ). However, it usually contains 5%–9% of C 2 H 6 residue, which must be reduced to ensure its utilization during polymerization. C 2 H 6 and C 2 H 4 have comparable kinetic diameters and boiling points (C 2 H 6 : 4.44, 184.55 K; C 2 H 4 : 4.16, 169.42 K), which makes the separation process very difficult. This contribution employs a methodology that integrates machine learning (ML) with Monte Carlo simulations to evaluate the ddmof database to develop a predictive model for separating ethane (C 2 H 6 ) and ethylene (C 2 H 4 ). The ML model's input is the metal–organic frameworks (MOFs) chemical and structural descriptors. The grand canonical Monte Carlo (GCMC) simulations in RASPA software were carried out to calculate the equilibrium adsorption of ethane and ethylene. Different ML models such as random forest, decision tree, and deep neural network models have been tested to estimate the selectivity and ethane uptake from the MOF data being generated. Interpretable ML model using SHapley Additive exPlanations (SHAP) is developed for the better understanding of the impact of the parameters on selectivity and ethane uptake. A user‐friendly graphical user interface ( GUI ) is presented, allowing users to predict the ethane uptake and selectivity of MOFs simply by entering the values of chemical and structural descriptors.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".