Exploring Optimal Water Splitting Bifunctional Alloy Catalyst by Pareto Active Learning

Abstract

reaction (OER) [8][9][10] at the anode. Several studies have reported the improvement in the catalytic performance of each reaction; still a simplified system as well as improvement in the overall water splitting performance of a full cell have been continuously proposed. [11,12] Moreover, the use of different catalysts for HER and OER can lead to cross-contamination of catalysts, thereby affecting the overall performance and stability of the reaction. [13,14] Therefore, bifunctional catalysts, which can be applied for efficient HER and OER, are investigated. Among various materials, multimetallic alloys such as RuNiCo, [15] IrNiCu, [16] AlNiCoIrMo, [17] and CoFeLaNiPt, [18] have showed high performances. These multimetallic alloys have been investigated considerably in catalysis society due to several advantages. Compared to monometallic catalysts, multimetallic alloy catalysts demonstrate an unexpected behavior or a synergistic effect. [19] In addition, expensive noble metals can be substituted by cheaper non-noble metals while still exhibiting comparable performance. [20,21] Meanwhile, few studies have determined the optimal content of bifunctional catalysts by tuning their components and composition.For these reasons, finding an optimal balance between the participating reactions, and thus designing multimetallic alloys Design of bifunctional multimetallic alloy catalysts, which are one of the most promising candidates for water splitting, is a significant issue for the efficient production of renewable energy. Owing to large dimensions of the components and composition of multimetallic alloys, as well as the tradeoff behavior in terms of the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) overpotentials for bifunctional catalysts, it is difficult to search for high-performance bifunctional catalysts with multimetallic alloys using conventional trial-and-error experiments. Here, an optimal bifunctional catalyst for water splitting is obtained by combining Pareto active learning and experiments, where 110 experimental data points out of 77946 possible points lead to effective model development. The as-obtained bifunctional catalysts for HER and OER exhibit high performance, which is revealed by model development using Pareto active learning; among the catalysts, an optimal catalyst (Pt 0.15 Pd 0.30 Ru 0.30 Cu 0.25 ) exhibits a water splitting behavior of 1.56 V at a current density of 10 mA cm −2 . This study opens avenues for the efficient exploration of multimetallic alloys, which can be applied in multifunctional catalysts as well as in other applications.

Accelerating materials research with a comprehensive data management tool: a case study on an electrochemical laboratory

“…To pave the way towards accelerated material discovery and optimization, currently novel systems are being developed ranging from high-throughput setups with human-in-the-loop 1–3 to fully self-driving laboratories. 4–6 Moreover, the rise of machine learning (ML) techniques supports this development by augmenting researchers and guiding experiments, 7,8 optimizing processes, 9 and even designing novel materials. 10,11 As these methods heavily rely on the availability and quality of data, collaboration throughout the scientific community will further enhance these developments.…”

In Situ Electrodeposition of Ultralow Pt into NiFe-Metal-Organic Framework/Nickel Foam Nanosheet Arrays as a Bifunctional Catalyst for Overall Water Splitting

“…On the one hand, using different catalysts for OER and HER may result in crosscontamination to decrease the activity of catalysts. 4 On the other hand, when different HER and OER electrodes are put in the same condition for the electrocatalytic reaction, these catalysts could also suffer from reduced activity or durability owing to unsatisfied pH environments, thus greatly hindering the commercial application. 5 Therefore, there is considerable potential for developing effective bifunctional electrocatalysts that can catalyze both the OER and HER under the same condition.…”

“…Although numerous scientists have worked to explore exceptional catalysts for boosting water electrolysis, more attention has been paid to improving the catalytic efficiency for merely one-half-reaction, which present several problems and challenges. On the one hand, using different catalysts for OER and HER may result in cross-contamination to decrease the activity of catalysts . On the other hand, when different HER and OER electrodes are put in the same condition for the electrocatalytic reaction, these catalysts could also suffer from reduced activity or durability owing to unsatisfied pH environments, thus greatly hindering the commercial application .…”

Designing Efficient Single-Atom Alloy Catalysts for Selective C═O Hydrogenation: A First-Principles, Active Learning and Microkinetic Study

“…DFT calculations enable the consideration of complicated potential energy surfaces involving various surface reactions, whereas ML allows for large-scale predictions and provides valuable insights into the structure–activity relationships of catalysts. Notably, active learning possesses the remarkable ability to substantially decrease the necessary training data size by multiple orders of magnitude, offering a convenient avenue for investigating complex chemical conundrums. − This development enables efficient exploration and understanding of intricate chemical problems with enhanced precision and a reduced computational burden. Therefore, the synergy between DFT calculations and active learning offers a promising new avenue for achieving breakthroughs in SAA design.…”