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 inACS applied materials & interfaces Vol. 16; no. 10; pp. 12563 - 12572
Main Authors Ai, Changzhi, Han, Shuang, Yang, Xin, Vegge, Tejs, Hansen, Heine Anton
Format Journal Article
LanguageEnglish
Published United States 13.03.2024
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ISSN1944-8244
1944-8252
DOI10.1021/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
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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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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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