The Role of Transition Metals on CeO<sub>2</sub>Supported for CO<sub>2</sub>Adsorption by DFT and Machine Learning Analysis
Bibliographic record
Abstract
Catalyst design is a field where machine learning (ML) algorithms have found many useful applications using atomistic simulation data sets. Atomistic simulation of CO 2 adsorption energy using several transition metals on ceria oxide (CeO 2 ) catalysts is the subject of our research. The density functional theory (DFT) calculation was used, and its results were applied as a data set to train several ML algorithms. Gibbs free energy (Δ G ) was simulated for all TMs such as Ni, Fe, Cu, Co, Mo, Ru, Rh, Pd, Ag, Pt, Zr, and Ti and used as the goal of CO 2 adsorption prediction using ML algorithms. The XGBoost algorithm provided satisfactory predictions of Δ G using different transition metals and bond energy at different adsorption temperatures on the catalysts. The effective role of Cu, Pt, Fe, and Ni in all temperature ranges regarding the increase of CO 2 adsorption energy was evident. The best CO 2 adsorption efficiency will be achieved by Ni at temperatures below 300 K and Cu at temperatures over 300 K. Evaluation of new catalysts using prediction findings and correlations between complex DFT simulation parameters demonstrates the potential of ML as a powerful tool for industry development.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".