2025/02/22 by Felix Thelen, Rico Zehl, Thelen, F. +11 · 1 citation
Chemical Engineering · Energy · Materials Science · #Catalysis and Oxidation Reactions #Computational Physics (physics.comp-ph) #Electrocatalysts for Energy Conversion #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci)
paper · pdf · doi:10.48550/arxiv.2502.16241
openalex publication_date 2025/02/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The discovery of high-performance electrocatalysts is crucial for advancing sustainable energy technologies. Compositionally complex solid solutions comprising multiple metals offer promising catalytic properties, yet their exploration is challenging due to the combinatorial explosion of possible compositions. In this work, we combine combinatorial sputtering of thin-film materials libraries and their high-throughput characterization with Bayesian optimization to efficiently explore the quaternary composition space Ni-Pd-Pt-Ru for the oxygen evolution reaction in alkaline media. Using this method, the global activity optimum of pure Ru was identified after covering less than 20% of the complete composition space with six materials libraries. Six additional libraries were fabricated to validate the activity trend. The resulting dataset is used to formulate general guidelines for the efficient composition space exploration using combinatorial synthesis paired with Bayesian optimization.