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
Published inJournal of Physical Chemistry C
© 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. ...
PublisherAmerican Chemical Society
Dataset(s) related to the publicationhttps://doi.org/10.17011/jyx/dataset/87521
Publication in research information system
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Related funder(s)Academy of Finland
Funding program(s)Others, AoF
Additional information about fundingThis 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.
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