TY - JOUR

T1 - Exploring the Composition Space of High-Entropy Alloy Nanoparticles for the Electrocatalytic H-2/CO Oxidation with Bayesian Optimization

AU - Mints, Vladislav A.

AU - Pedersen, Jack K.

AU - Bagger, Alexander

AU - Quinson, Jonathan

AU - Anker, Andy S.

AU - Jensen, Kirsten M. O.

AU - Rossmeisl, Jan

AU - Arenz, Matthias

PY - 2022

Y1 - 2022

N2 - High-entropy alloy (HEA) electrocatalysts offer a vast composition space that awaits exploration to identify interesting materials for energy conversion reactions. While attempts have been made to explore the composition space of HEA thin-film libraries and compare experimental and computational studies, no corresponding approaches exist for HEA nanoparticles. So far, catalytic investigations on HEA nanoparticles are limited to small sets of individual catalysts. Here, we report the experimental exploration of the composition space of carbon-supported Pt-Ru-Pd-Rh- Au nanoparticles for the H-2/CO oxidation reaction by constructing a dataset using Bayesian optimization as guidance. Applying a surfactant-free synthesis platform, a dataset of 68 samples was investigated. By constructing machine learning models, the relationship between the concentrations of the constituent elements and the catalytic activity was analyzed and compared to density functional theory calculations. The machine learning models confirm findings from previous studies concerning the role of Ru in the H-2/CO oxidation reaction. This has been achieved starting from a random set of compositions and without any prior assumptions for the reaction mechanism nor any in-depth design of the active site. In addition, by comparing the trends of the computational and experimental studies, it is seen that the "onset potentials " across the compositions can be correlated with the adsorption energy of *OH. The best correlation between the computational and experimental data is obtained when considering 5% of the most strongly *OH adsorbing sites.

AB - High-entropy alloy (HEA) electrocatalysts offer a vast composition space that awaits exploration to identify interesting materials for energy conversion reactions. While attempts have been made to explore the composition space of HEA thin-film libraries and compare experimental and computational studies, no corresponding approaches exist for HEA nanoparticles. So far, catalytic investigations on HEA nanoparticles are limited to small sets of individual catalysts. Here, we report the experimental exploration of the composition space of carbon-supported Pt-Ru-Pd-Rh- Au nanoparticles for the H-2/CO oxidation reaction by constructing a dataset using Bayesian optimization as guidance. Applying a surfactant-free synthesis platform, a dataset of 68 samples was investigated. By constructing machine learning models, the relationship between the concentrations of the constituent elements and the catalytic activity was analyzed and compared to density functional theory calculations. The machine learning models confirm findings from previous studies concerning the role of Ru in the H-2/CO oxidation reaction. This has been achieved starting from a random set of compositions and without any prior assumptions for the reaction mechanism nor any in-depth design of the active site. In addition, by comparing the trends of the computational and experimental studies, it is seen that the "onset potentials " across the compositions can be correlated with the adsorption energy of *OH. The best correlation between the computational and experimental data is obtained when considering 5% of the most strongly *OH adsorbing sites.

KW - high-entropy alloy nanoparticles

KW - H-2/CO oxidation reaction

KW - electrocatalysis

KW - machine learning

KW - HYDROGEN OXIDATION

KW - METHANOL OXIDATION

KW - OXYGEN REDUCTION

KW - CO

KW - FUEL

KW - CATALYSTS

KW - ADSORPTION

KW - ELECTRODES

KW - DEPENDENCE

KW - EVOLUTION

U2 - 10.1021/acscatal.2c02563

DO - 10.1021/acscatal.2c02563

M3 - Journal article

VL - 12

SP - 11263

EP - 11271

JO - ACS Catalysis

JF - ACS Catalysis

SN - 2155-5435

IS - 18

ER -