Published October 5, 2021 | Version v1
Journal article Open

Bayesian Optimization of High-Entropy Alloy Compositions for Electrocatalytic Oxygen Reduction

Authors/Creators

  • 1. Center for High Entropy Alloy Catalysis (CHEAC) Department of Chemistry, University of Copenhagen Universitetsparken 5, 2100 København Ø (Denmark)
  • 2. Center for Electrochemical Sciences (CES) Faculty of Chemistry and Biochemistry Ruhr University Bochum Universitätsstrasse 150, 44780 Bochum (Germany)
  • 3. Institute for Materials, Faculty of Mechanical Engineering, Ruhr University Bochum Universitätsstrasse 150, 44780 Bochum (Germany)
  • 4. Center for High Entropy Alloy Catalysis (CHEAC) Department of Chemistry, Biochemistry and Pharmaceutical Sciences University of Bern Freiestrasse 3, 3012 Bern (Switzerland)

Description

Active, selective and stable catalysts are imperative for sustainable energy conversion, and engineering materials with such properties are highly desired. High-entropy alloys (HEAs) offer a vast compositional space for tuning such properties. Too vast, however, to traverse without the proper tools. Here, we report the use of Bayesian optimization on a model based on density functional theory (DFT) to predict the most active compositions for the electrochemical oxygen reduction reaction (ORR) with the least possible number of sampled compositions for the two HEAs Ag-Ir-Pd-Pt-Ru and Ir-Pd-Pt-Rh-Ru. The discovered optima are then scrutinized with DFT and subjected to experimental validation where optimal catalytic activities are verified for Ag–Pd, Ir–Pt, and Pd–Ru binary alloys. This study offers insight into the number of experiments needed for optimizing the vast compositional space of multimetallic alloys which has been determined to be on the order of 50 for ORR on these HEAs.

Notes

J.P., C.C., V.M., T.B., M.A., and J.R. acknowledge support from the Danish National Research Foundation Center for High Entropy Alloy Catalysis (CHEAC) DNRF-149. J.P. acknowledges support from the Danish Ministry of Higher Education and Science (Structure of Materials in Real Time (SMART) grant), T.B. acknowledges support from VILLUM FONDEN (research grant 9455), W.S. acknowledges funding from Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy (EXC 2033-390677874—RESOLV) and from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement CasCat [833408]. A.L. and B.X. acknowledge funding from DFG project LU1175/26-1. ZGH at RUB is acknowledged for use of its experimental facilities.

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DOI10.1002anie.202108116.pdf

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Additional details

Funding

European Commission
CASCAT - Catalytic cascade reactions. From fundamentals of nanozymes to applications based on gas-diffusion electrodes 833408