Publication date: 15th December 2025
Practical electrocatalyst deployment requires balancing performance across multiple, often conflicting objectives. We demonstrate this for the acidic oxygen reduction reaction (ORR) where tradeoffs between activity, stability, and material cost must be considered. To navigate this, we employ a multi-objective Bayesian optimization framework that utilizes the continuous design space of high-entropy alloys (HEAs). The activity and stability are assessed through our existing machine-learning driven models for catalytic activity[1] and electrochemical dissolution[2]. In simulating the activity, we will present a machine-learning model for adsorption energy inference on alloys finetuned on an extensive density functional theory HEA dataset that covers 12 elements and 9 adsorbates. Within our framework, we uncover a novel activity-stability-cost Pareto front for ORR for the Ag-Au-Cu-Ir-Pd-Pt-Rh-Ru composition space. We find that alloying expands the hypervolume spanned by the Pareto front, and that the front is constituted of low-entropic alloys consisting primarily of Ag, Au, Cu, Pd, and Pt. Moreover, we present a new approach to analyzing these optimal trade-offs by investigating the hypervolume degradation upon removing critical elements (Au, Pd, and Pt), revealing their individual roles in the Pareto front. This work highlights the necessity of considering all relevant objectives in catalyst optimization and the advantage of HEAs as a platform for multi-objective catalyst discovery.
The authors acknowledge funding from ERC Synergy grant DEMI, GA no. 101118768 and The Center for High Entropy Alloy Catalysis (CHEAC) supported by the Danish National Research Foundation (DNRF149).
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