TY - JOUR

T1 - Exploring the High-Entropy Oxide Composition Space

T2 - Insights through Comparing Experimental with Theoretical Models for the Oxygen Evolution Reaction

AU - Mints, Vladislav A.

AU - Svane, Katrine L.

AU - Rossmeisl, Jan

AU - Arenz, Matthias

N1 - Funding Information: This work is supported by the Danish National Research Foundation Center for High-Entropy Alloy Catalysis (CHEAC) DNRF-149. Publisher Copyright: © 2024 The Authors. Published by American Chemical Society.

PY - 2024

Y1 - 2024

N2 - The oxygen evolution reaction (OER) is key for the transition to a hydrogen-based energy economy. The observed activity of the OER catalysts arises from the combined effects of surface area, intrinsic activity, and stability. Therefore, alloys provide an effective platform to search for catalysts that balance these factors. In particular, high-entropy oxides provide a vast material composition space that could contain catalysts with optimal OER performance. In this work, the OER performance of the AuIrOsPdPtReRhRu composition space was modeled using an experimentally obtained dataset of 350 nanoparticles. This machine-learned model based on experimental data found the optimal catalyst to be a mixture of AuIrOsPdRu. However, as a “black-box model”, it cannot explain the underlying chemistry. Therefore, density functional theory (DFT) calculations were performed to provide a complementary theoretical model with defined assumptions and, hence, a physical interpretation through comparison with the experimental model. The DFT calculations suggest that the majority of the activity originates from Ru and Ir active sites and that the addition of Pd improves the performance of these sites. However, the DFT calculation did not find the experimentally observed beneficial effects of Au and Os. Therefore, we hypothesize that the Os contributed to the performance of the tested catalysts by roughening the surface, whereas Au fulfilled the role of a structural support. Overall, it is demonstrated how machine learning can help accelerate catalyst discovery, and combining machine-learned models obtained from experimental data with models based on DFT calculations can provide important insights into the complex chemistry of OER catalysts.

AB - The oxygen evolution reaction (OER) is key for the transition to a hydrogen-based energy economy. The observed activity of the OER catalysts arises from the combined effects of surface area, intrinsic activity, and stability. Therefore, alloys provide an effective platform to search for catalysts that balance these factors. In particular, high-entropy oxides provide a vast material composition space that could contain catalysts with optimal OER performance. In this work, the OER performance of the AuIrOsPdPtReRhRu composition space was modeled using an experimentally obtained dataset of 350 nanoparticles. This machine-learned model based on experimental data found the optimal catalyst to be a mixture of AuIrOsPdRu. However, as a “black-box model”, it cannot explain the underlying chemistry. Therefore, density functional theory (DFT) calculations were performed to provide a complementary theoretical model with defined assumptions and, hence, a physical interpretation through comparison with the experimental model. The DFT calculations suggest that the majority of the activity originates from Ru and Ir active sites and that the addition of Pd improves the performance of these sites. However, the DFT calculation did not find the experimentally observed beneficial effects of Au and Os. Therefore, we hypothesize that the Os contributed to the performance of the tested catalysts by roughening the surface, whereas Au fulfilled the role of a structural support. Overall, it is demonstrated how machine learning can help accelerate catalyst discovery, and combining machine-learned models obtained from experimental data with models based on DFT calculations can provide important insights into the complex chemistry of OER catalysts.

KW - density functional theory (DFT) calculations

KW - electrochemistry

KW - high entropy oxides

KW - machine learning

KW - oxygen evolution reaction

U2 - 10.1021/acscatal.3c05915

DO - 10.1021/acscatal.3c05915

M3 - Journal article

AN - SCOPUS:85191092620

VL - 14

SP - 6936

EP - 6944

JO - ACS Catalysis

JF - ACS Catalysis

SN - 2155-5435

IS - 9

ER -