Searching for an Optimal Multi-Metallic Alloy Catalyst by Active Learning Combined with Experiments

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Searching for an optimal component and composition of multi-metallic alloy catalysts, comprising two or more elements, is one of the key issues in catalysis research. Due to the exhaustive data requirement of conventional machine-learning (ML) models and the high cost of experimental trials, current approaches rely mainly on the combination of density functional theory and ML techniques. In this study, a significant step is taken toward overcoming limitations by the interplay of experiment and active learning to effectively search for an optimal component and composition of multi-metallic alloy catalysts. The active-learning model is iteratively updated using by examining electrocatalytic performance of fabricated solid-solution nanoparticles for the hydrogen evolution reaction (HER). An optimal metal precursor composition of Pt0.65Ru0.30Ni0.05 exhibits an HER overpotential of 54.2 mV, which is superior to that of the pure Pt catalyst. This result indicates the successful construction of the model by only utilizing the precursor mixture composition as input data, thereby improving the overpotential by searching for an optimal catalyst. This method appears to be widely applicable since it is able to determine an optimal component and composition of electrocatalyst without obvious restriction to the types of catalysts to which it can be applied.
Publisher
WILEY-V C H VERLAG GMBH
Issue Date
2022-05
Language
English
Article Type
Article
Citation

ADVANCED MATERIALS, v.34, no.19, pp.2108900

ISSN
0935-9648
DOI
10.1002/adma.202108900
URI
http://hdl.handle.net/10203/296678
Appears in Collection
MS-Journal Papers(저널논문)CBE-Journal Papers(저널논문)
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