Complex‐Solid‐Solution Electrocatalyst Discovery by Computational Prediction and High‐Throughput Experimentation. Issue 13 (10th February 2021)

• Record Type: Journal Article
• Authors: Batchelor, Thomas A. A.; Löffler, Tobias; Xiao, Bin; Krysiak, Olga A.; Strotkötter, Valerie; Pedersen, Jack K.; Clausen, Christian M.; Savan, Alan; Li, Yujiao; Schuhmann, Wolfgang; Rossmeisl, Jan; Ludwig, Alfred
• Is Part Of: Angewandte Chemie international edition. Volume 60:Issue 13(2021)
• Page Start: 6932
• Page End: 6937
• Publication Date: 2021-02-10
• Subjects: density functional calculations -- electrochemistry -- high-entropy alloys -- high-throughput screening -- thin films; Chemistry -- Periodicals; 540
• DOI: 10.1002/anie.202014374
• Languages: English
• ISSNs: 1433-7851

Abstract

Abstract: Complex solid solutions ("high entropy alloys"), comprising five or more principal elements, promise a paradigm change in electrocatalysis due to the availability of millions of different active sites with unique arrangements of multiple elements directly neighbouring a binding site. Thus, strong electronic and geometric effects are induced, which are known as effective tools to tune activity. With the example of the oxygen reduction reaction, we show that by utilising a data‐driven discovery cycle, the multidimensionality challenge raised by this catalyst class can be mastered. Iteratively refined computational models predict activity trends around which continuous composition‐spread thin‐film libraries are synthesised. High‐throughput characterisation datasets are then used as input for refinement of the model. The refined model correctly predicts activity maxima of the exemplary model system Ag‐Ir‐Pd‐Pt‐Ru. The method can identify optimal complex‐solid‐solution materials for electrocatalytic reactions in an unprecedented manner. Abstract : Complex solid solutions ("high‐entropy alloys") promise a paradigm change in electrocatalysis but expose the challenge of almost unlimited options in adjusting their compositions. We propose the utilisation of computational models, combined with high‐throughput experimentation for the verification of the model assumptions, which allows for model refinement in iterative loops, understanding of binding mechanisms, and discovery of the most active composition.