Complex solid solution electrocatalyst discovery by prediction and high-throughput experimentation

Efficient discovery of electrocatalysts for electrochemical energy conversion reactions is of utmost importance to combat climate change. With the example of the oxygen reduction reaction we show that by utilising a data-driven discovery cycle, the multidimensionality challenge offered by compositio...

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Published inarXiv.org
Main Authors Batchelor, Thomas A A, Löffler, Tobias, Xiao, Bin, Krysiak, Olga A, Strotkötter, Valerie, Pedersen, Jack K, Clausen, Christian M, Savan, Alan, Schuhmann, Wolfgang, Rossmeisl, Jan, Ludwig, Alfred
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 17.09.2020
Subjects
Chemical reactions
Chemical reduction
Composition
Electrocatalysts
Energy conversion
Entropy of solution
Experimentation
High entropy alloys
Iridium
Palladium
Physics - Materials Science
Platinum
Silver
Solid solutions
Thin films
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Summary:Efficient discovery of electrocatalysts for electrochemical energy conversion reactions is of utmost importance to combat climate change. With the example of the oxygen reduction reaction we show that by utilising a data-driven discovery cycle, the multidimensionality challenge offered by compositionally complex solid solution (high entropy alloy) electrocatalysts can be mastered. Iteratively refined computational models predict activity trends for quinary target compositions, around which continuous composition spread thin-film libraries are synthesized. High-throughput characterisation datasets are then input for refinement of the model. The refined model correctly predicts activity maxima of the exemplary model system Ag-Ir-Pd-Pt-Ru for the oxygen reduction reaction. The method can identify optimal complex solid solutions for electrochemical reactions in an unprecedented manner.
ISSN:2331-8422
DOI:10.48550/arxiv.2009.08529