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High entropy alloys offer a huge search space for new electrocatalysts.Searching for a global property maximum in one quinary system could require,depending on compositional resolution, the synthesis of up to 10E6 sampleswhich is impossible using conventional approaches. Co-sputtered materialslibraries address this challenge by synthesis of controlled compositiongradients of each element. However, even such a materials library covers lessthan 1% of the composition space of a quinary system. We present a new strategyusing deposition source permutations optimized for highest improvement of thecovered new compositions. Using this approach, the composition space can besampled in different subsections allowing identification of the contribution ofindividual elements and their combinations on electrochemical activity.Unsupervised machine learning reveals that electrochemical activity is governedby the complex interplay of chemical and structural factors. Out of 2394measured compositions, a new highly active composition for the oxygen reductionreaction around Ru17Rh5Pd19Ir29Pt30 was identified.

Combinatorial materials discovery strategy for high entropy alloy electrocatalysts using deposition source permutations