Following Paths of Maximum Catalytic Activity in the Composition Space of High‐Entropy Alloys

Abstract The search for better and cheaper electrocatalysts is vital in the global transition to renewable energy resources. High‐entropy alloys (HEAs) provide a near‐infinite number of different alloys with approximately continuous properties such as catalytic activity. In this work, the catalytic activity for the electrochemical oxygen reduction reaction as a function of molar composition of Ag‐Ir‐Pd‐Pt‐Ru HEA is treated as a landscape wherein it is shown that the maxima are connected through ridges. By following the ridges, it is possible to navigate between the maxima using a modified nudged elastic band (NEB) model integrated in a machine learning NEB algorithm. These results provide a new understanding of the composition space being similar to an evolutionary landscape. This provides a possible new search and design strategy for new catalysts in which the composition of known catalysts can be optimized by following ridges rather than exploring the whole alloy composition space.