Deep Learning Guided Exploration of Transition Metal Oxide Catalysts in Acetylene Selective Hydrogenation
Bibliographic record
Abstract
The development of empirical materials, hindered by the complex interplay between material properties and reaction mechanisms, typically necessitates an extensive trial-and-error process to identify optimal catalysts. In our study, we integrate density functional theory (DFT) predictions to generate mappings of electronic and molecular adsorption properties, which are then employed to identify novel materials using deep learning algorithms. These newly identified materials were synthesized and assessed for their catalytic performance in the selective hydrogenation of acetylene. Notably, CuTiO 3 catalysts demonstrated exceptional performance, achieving acetylene conversion exceeding 99% and ethylene selectivity greater than 99% at a relatively low temperature of 75 °C. Additionally, CuO-doped TiO 2 was observed to form strong acetylene adsorption sites and weaker ethylene adsorption sites (Cu–O–Ti). The p–π hybridized coupling between the oxygen p-orbitals and the π-electrons of acetylene in the Cu–O–Ti structure was found to play a critical role in facilitating the conversion of acetylene to ethylene.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".