A1 Journal article (refereed)
Graphs and Kernelized Learning Applied to Interactions of Hydrogen with Doped Gold Nanoparticle Electrocatalysts (2023)

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

JYU authors or editors

Publication details

All authors or editors: Pihlajamäki, Antti; Malola, Sami; Kärkkäinen, Tommi; Häkkinen, Hannu

Journal or series: Journal of Physical Chemistry C

ISSN: 1932-7447

eISSN: 1932-7455

Publication year: 2023

Publication date: 18/07/2023

Volume: 127

Issue number: 29

Pages range: 14211-14221

Publisher: American Chemical Society

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1021/acs.jpcc.3c02539

Research data link: https://doi.org/10.17011/jyx/dataset/87521

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/88949

Additional information: Published as part of The Journal of Physical Chemistry C virtual special issue “Machine Learning in Physical Chemistry, Volume 2".

Abstract

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.

Keywords: machine learning; adsorption; gold; hydrogen; water; nanoparticles

Free keywords: interaction energies

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Ministry reporting: Yes

Reporting Year: 2023

Preliminary JUFO rating: 2