Complex-solid-solution electrocatalyst discovery by computational prediction and high-throughput experimentation
Batchelor, Thomas A. A. (Dr. med.), Löffler, Tobias (Dr. rer. nat.), Xiao, Bin (Dr. rer. nat.), Krysiak, Olga A. (Dr. rer. nat.), Strotkötter, Valerie (M. Sc.), Pedersen, Jack K. (Dr. rer. nat.), Clausen, Christian M. (Dr. rer. nat.), Savan, Alan (M. Sc.), Li, Yujiao (Dr.), Schuhmann, Wolfgang (Prof. Dr. rer nat.), Rossmeisl, Jan (Prof. Dr.), Ludwig, Alfred (Prof. Dr.-Ing.)
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.