Graph Neural Network-Accelerated Multitasking Genetic Algorithm for Optimizing Pd x Ti 1– x H y Surfaces under Various CO 2 Reduction Reaction Conditions
Palladium (Pd) hydride-based catalysts have been reported to have excellent performance in the CO reduction reaction (CO RR) and hydrogen evolution reaction (HER). Our previous work on doped PdH and Pd alloy hydrides showed that Ti-doped and Ti-alloyed Pd hydrides could improve the performance of th...
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| Published in | ACS applied materials & interfaces Vol. 16; no. 10; pp. 12563 - 12572 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
13.03.2024
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 1944-8244 1944-8252 |
| DOI | 10.1021/acsami.3c18734 |
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| Abstract | Palladium (Pd) hydride-based catalysts have been reported to have excellent performance in the CO
reduction reaction (CO
RR) and hydrogen evolution reaction (HER). Our previous work on doped PdH and Pd alloy hydrides showed that Ti-doped and Ti-alloyed Pd hydrides could improve the performance of the CO
reduction reaction compared with pure Pd hydride. Compositions and chemical orderings of the surfaces with only one adsorbate under certain reaction conditions are linked to their stability, activity, and selectivity toward the CO
RR and HER, as shown in our previous work. In fact, various coverages, types, and mixtures of the adsorbates, as well as state variables such as temperature, pressure, applied potential, and chemical potential, could impact their stability, activity, and selectivity. However, these factors are usually fixed at common values to reduce the complexity of the structures and the complexity of the reaction conditions in most theoretical work. To address the complexities above and the huge search space, we apply a deep learning-assisted multitasking genetic algorithm to screen for Pd
Ti
H
surfaces containing multiple adsorbates for CO
RR under different reaction conditions. The ensemble deep learning model can greatly speed up the structure relaxations and retain a high accuracy and low uncertainty of the energy and forces. The multitasking genetic algorithm simultaneously finds globally stable surface structures under each reaction condition. Finally, 23 stable structures are screened out under different reaction conditions. Among these, Pd
Ti
H
+ 25%CO, Pd
Ti
H
+ 50%CO, Pd
Ti
H
+ 25%CO, and Pd
Ti
H
+ 25%CO are found to be very active for CO
RR and suitable to generate syngas consisting of CO and H
. |
|---|---|
| AbstractList | Palladium (Pd) hydride-based catalysts have been reported to have excellent performance in the CO
reduction reaction (CO
RR) and hydrogen evolution reaction (HER). Our previous work on doped PdH and Pd alloy hydrides showed that Ti-doped and Ti-alloyed Pd hydrides could improve the performance of the CO
reduction reaction compared with pure Pd hydride. Compositions and chemical orderings of the surfaces with only one adsorbate under certain reaction conditions are linked to their stability, activity, and selectivity toward the CO
RR and HER, as shown in our previous work. In fact, various coverages, types, and mixtures of the adsorbates, as well as state variables such as temperature, pressure, applied potential, and chemical potential, could impact their stability, activity, and selectivity. However, these factors are usually fixed at common values to reduce the complexity of the structures and the complexity of the reaction conditions in most theoretical work. To address the complexities above and the huge search space, we apply a deep learning-assisted multitasking genetic algorithm to screen for Pd
Ti
H
surfaces containing multiple adsorbates for CO
RR under different reaction conditions. The ensemble deep learning model can greatly speed up the structure relaxations and retain a high accuracy and low uncertainty of the energy and forces. The multitasking genetic algorithm simultaneously finds globally stable surface structures under each reaction condition. Finally, 23 stable structures are screened out under different reaction conditions. Among these, Pd
Ti
H
+ 25%CO, Pd
Ti
H
+ 50%CO, Pd
Ti
H
+ 25%CO, and Pd
Ti
H
+ 25%CO are found to be very active for CO
RR and suitable to generate syngas consisting of CO and H
. |
| Author | Vegge, Tejs Han, Shuang Yang, Xin Hansen, Heine Anton Ai, Changzhi |
| Author_xml | – sequence: 1 givenname: Changzhi surname: Ai fullname: Ai, Changzhi organization: Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, 2800 Kongens Lyngby, Denmark – sequence: 2 givenname: Shuang surname: Han fullname: Han, Shuang organization: Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, 2800 Kongens Lyngby, Denmark – sequence: 3 givenname: Xin surname: Yang fullname: Yang, Xin organization: Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, 2800 Kongens Lyngby, Denmark – sequence: 4 givenname: Tejs orcidid: 0000-0002-1484-0284 surname: Vegge fullname: Vegge, Tejs organization: Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, 2800 Kongens Lyngby, Denmark – sequence: 5 givenname: Heine Anton orcidid: 0000-0001-7551-9470 surname: Hansen fullname: Hansen, Heine Anton organization: Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, 2800 Kongens Lyngby, Denmark |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38437157$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1002_wcms_70010 crossref_primary_10_1063_5_0227821 crossref_primary_10_1016_j_ccr_2025_216541 |
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| Keywords | genetic algorithm deep learning evolutionary multitasking surface free energy PdxTi1−xHy graph neural network CO2 reduction global optimization |
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reduction reaction (CO
RR) and hydrogen evolution reaction... |
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| Title | Graph Neural Network-Accelerated Multitasking Genetic Algorithm for Optimizing Pd x Ti 1– x H y Surfaces under Various CO 2 Reduction Reaction Conditions |
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