Discovering high entropy alloy electrocatalysts in vast composition spaces with multi-objective optimization

High entropy alloys (HEAs) are a highly promising class of materials for electrocatalysis, as their unique active site distributions break the scaling relations that limit the activity of conventional transition metal catalysts. Existing Bayesian optimization (BO) based virtual screening approaches focus on catalytic activity as sole objective and correspondingly tend to identify promising materials that are unlikely to be entropically stabilized. Here, we overcome this limitation with a multi-objective BO framework for HEAs that simultaneously targets activity, cost-effectiveness and entropic stabilization. With a diversity-guided batch selection further boosting the data efficiency, the framework readily identifies numerous promising candidates for the oxygen reduction reaction that strike the balance between all three objectives in hitherto unchartered HEA design spaces comprising up to ten elements.