In Advanced materials (Deerfield Beach, Fla.)
Design of bifunctional multi-metallic alloy catalysts, which are one of the most promising candidates for water splitting, has been a significant issue for the efficient production of renewable energy. Owing to large dimensions of the components and composition of multi-metallic alloys, as well as the trade-off behavior in terms of the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) overpotential for bifunctional catalysts, it is difficult to search for high-performance bifunctional catalysts with multi-metallic alloys using conventional trial-and-error experiments. In this study, an optimal bifunctional catalyst for water splitting was obtained by combining Pareto active learning and experiments, where 110 experimental data points out of 77,946 possible points led to effective model development. The as-obtained bifunctional catalysts for HER and OER exhibited high performance, which was revealed by model development using Pareto active learning; among the catalysts, an optimal catalyst (Pt0.15 Pd0.30 Ru0.30 Cu0.25 ) exhibited a water splitting behavior of 1.56 V at a current density of 10 mA/cm2 . This study opens avenues for the efficient exploration of multi-metallic alloys, which can be applied in multifunctional catalysts as well as in other applications. This article is protected by copyright. All rights reserved.
Kim Minki, Kim Yesol, Ha Min Young, Shin Euichul, Kwak Seung Jae, Park Minhee, Kim Il-Doo, Jung Woo-Bin, Lee Won Bo, Kim YongJoo, Jung Hee-Tae
2023-Feb-10
Bifunctional catalyst, Machine learning, Multi-metallic alloy, Pareto active learning, Water splitting
