Graphs and Kernelized Learning Applied to Interactions of Hydrogen with Doped Gold Nanoparticle Electrocatalysts
Pihlajamäki, A., Malola, S., Kärkkäinen, T., & Häkkinen, H. (2023). Graphs and Kernelized Learning Applied to Interactions of Hydrogen with Doped Gold Nanoparticle Electrocatalysts. Journal of Physical Chemistry C, 127(29), 14211-14221. https://doi.org/10.1021/acs.jpcc.3c02539
Julkaistu sarjassa
Journal of Physical Chemistry CPäivämäärä
2023Oppiaine
Nanoscience CenterTekniikkaHuman and Machine based Intelligence in LearningNanoscience CenterEngineeringHuman and Machine based Intelligence in LearningTekijänoikeudet
© 2023 the Authors
Understanding hydrogen adsorption on metal nanoparticles is a key prerequisite for designing efficient electrocatalysts for water splitting and the hydrogen evolution reaction. However, this seemingly simple elementary reaction step is affected by several factors arising from the chemical environment at the catalyst, and deciphering the most important contributions to optimal interactions requires numerically heavy electronic structure calculations. Here, we combine graph-based representations of the local atomic environment of hydrogen in copper- and palladium-doped 25-atom gold nanoparticles with several kernel-based machine learning (ML) methods to predict the interaction energy between hydrogen and the nanoparticle catalyst. We demonstrate that simple distance-based kernel models are able to predict the interaction energy within 0.1 eV when trained by reference data from state-of-the-art density functional theory calculations. Analyzing the model performance with respect to attributes of the hydrogen node highlights the locality of hydrogen adsorption. This implies the viability of combining graphs with kernel-based ML models for studying hydrogen chemisorption in complex environment data efficiently.
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Julkaisija
American Chemical SocietyISSN Hae Julkaisufoorumista
1932-7447Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/184057072
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This work was supported by the Academy of Finland through grants 351582 and 351579 in the Euro HPC Research Programme. Computations were done at the FCCI node in the University of Jyväskylä (persistent identifier: urn:nbn:fi:research-infras-2016072533).The authors acknowledge J. Linja for discussions on methodology and O. López-Estrada, N. Mammen, and L. Laverdure for providing the DFT data.Lisenssi
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