Rational design of high-entropy alloy nanoparticle catalysts through high-throughput composition space screening

Despite the promising catalytic performance of high-entropy alloy (HEA) nanomaterials, their computational rational design remains challenging due to the complexity of the atomic arrangements and the vast composition space. In this work, we developed an approach utilizing a machine-learning cluster expansions model to conduct the computational high-throughput screening of the quinary alloy composition space of HEA nanocatalysts, sampled at 5% composition intervals. This approach allows for the identification of alloy compositions that maximize catalytic activities. Metropolis Monte Carlo simulations, based on cluster expansion Hamiltonian, were employed to predict the thermodynamic equilibrium nanoparticle structures and average turnover frequencies of all surface sites. We applied this approach to Ir－Pd－Pt－Rh－Ru octahedral nanoparticles as oxygen reduction reaction (ORR) catalysts, disclosing that the predicted ORR activity is maximized by synthesizing the HEA nanoparticles with relatively high Pt and Ru compositions and minimized Pd and Rh compositions. The approach developed in this work is well-suited for the investigation of HEA nanocatalysts, thereby facilitating the rational design of these catalysts.