Graph Neural Network-Accelerated Multitasking Genetic Algorithm for Optimizing Pd<sub><i>x</i></sub>Ti<sub>1-<i>x</i></sub>H<sub><i>y</i></sub> Surfaces under Various CO<sub>2</sub> Reduction Reaction Conditions
Bibliographic record
Abstract
Palladium (Pd) hydride-based catalysts have been reported to have excellent performance for CO2 reduction (CO2RR) and hydrogen evolution reactions (HER). Our previous doped-PdH and alloy hydride works showed that Ti-doped and Ti-alloyed Pd hydride could improve the performance of the CO2 reduction reaction compared to pure Pd hydride. Compositions and chemical orderings of the surfaces with only one adsorbate under certain reaction condition are linked to their stablity, activity, and selectivity towards CO2RR and HER in our previous work. In fact, various coverages, types, and mixture of the adsorbates as well as state variable such as, temperature, pressure, applied potential, and chemical potential could have an impact on their stability, activity, and selectivity. However, these factors are usually fixed at common values in order to reduce the complexity of structures and complexity of reaction conditions in most theoretical work. To address the above complexities and thus huge search space, a deep learning-assisted multitasking genetic algorithm is used to screen for PdxTi1-xHy surfaces containing multiple adsorbates for CO2RR under different reaction conditions in this work. The ensemble deep learning model can greatly speed up the structure relaxations and keep a high accuracy and low uncertainty of energy and forces. The multitasking genetic algorithm is used to simultaneously globally find stable surface structures at each reaction condition. Finally, 23 stable structures are screened out under different reaction conditions. Among them, Pd0.56Ti0.44H1.06+25%CO, Pd0.31Ti0.69H1.25+50%CO, Pd0.31Ti0.69H1.25+25%CO, and Pd0.88Ti0.12H1.06+25%CO are found to be very active for CO2RR and suitable to generate syngas consisting of CO2 and H2.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".