Backward Elimination: A Strategy for High-Entropy Alloy Catalyst Discovery

A promising opportunity and major challenge in the field of High-Entropy Alloy (HEA) catalysis is the abundance of possible compositions. The number of possible compositions makes it impossible to study all of them. Therefore, sophisticated methods are required to intelligently select interesting compositions. On one hand, adding an element to a composition space increases the dimensionality of that space and the number of compositions within it combinatorially. However, it also increases the number of sub-spaces that are part of this larger composition space. Assuming a constant sampling density, the number of experiments required to study a large, combined composition space of sufficient dimensionality can be less than studying all of its individual sub-spaces. This hypothesis is investigated using experimental work in which 200 compositions in an 8-element composition space composed of Au, Ir, Os, Pd, Pt, Re, Rh, and Ru were synthesized as nanoparticles. Each composition was experimentally tested for the electrocatalytic activity towards the oxygen reduction reaction. The model that was constructed using this data turned out to adequately predict data in three of its 5-element composition sub-spaces. This observation paves way for a backward elimination strategy in HEA discovery. According to this strategy all elements of interest are studied in a single composition space from which knowledge of all subspaces can be learnt. Subsequently, elements that do not show a positive influence towards the studied reaction can be removed from the search space. Ultimately, this backward elimination search produces an alloy space which has a high probability of containing the most active catalyst composition.