High-Throughput Experimentation for Accelerated Discovery of PGM-Free Catalysts for Anion-Exchange Membrane Water Electrolyzers

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High-throughput experimentation is a powerful tool for accelerating the discovery and optimization of high-performance PGM-free oxygen evolution reaction (OER) catalysts for anion-exchange membrane water electrolysis. The sluggish OER kinetics limit several critically important sustainable energy technologies, including water electrolysis, metal-air batteries, and the electrochemical CO 2 reduction reaction (CO 2 RR). While platinum group metal (PGM) catalysts like IrO 2 and RuO 2 exhibit high OER activity in proton exchange membrane water electrolysis (PEMWE), their scarcity and cost hinder widespread deployment. Anion exchange membrane water electrolysis (AEMWE) emerges as a promising technology with the potential to be both cost-effective and sustainable for hydrogen production. It relies on PGM-free OER catalysts, merging the strengths of PEM and traditional alkaline electrolysis systems. Transition metal (TM) oxides, layered double hydroxides, and oxyphosphides, particularly in alkaline media, have emerged as promising OER catalysts due to their high activity and durability. However, their vast compositional and synthetic parameter space necessitates efficient exploration strategies. Traditional trial-and-error approaches are time-consuming and resource-intensive. This work integrates high-throughput experimentation with adaptive machine learning to address this challenge. High-throughput synthesis and characterization techniques enable the rapid generation of experimental data on a diverse library of oxyphosphide catalysts. This data can then be used to train computational models that surrogate structure-property relationships. By coupling these models with Bayesian optimization strategies, one can then intelligently and optimally select subsequent experiments based on predicted performance and associated uncertainty, iteratively refining the model and guiding the exploration towards optimal catalyst compositions. This closed-loop approach minimizes the number of experiments required to identify promising candidates, significantly accelerating the materials discovery process. This presentation will summarize high-throughput experimentation for optimizing the parameters for synthesis of OER activity in alkaline media. At different stages the high-throughput methodology has been guided by adaptive learning. The key parameters undergoing optimization have been the metal ratios and annealing conditions. In this presentation, we will also introduce a newly proposed activity descriptor for nickel-based PGM-free catalysts for alkaline OER. This approach promises to expedite the development of cost-effective and efficient PGM-free OER catalysts for sustainable hydrogen production via alkaline water electrolysis. Acknowledgments This work was supported by the U.S. Department of Energy (DOE), Energy Efficiency and Renewable Energy, Hydrogen and Fuel Cell Technologies Office (HFTO) under the auspices of the Electrocatalysis Consortium (ElectroCat 2.0). Argonne is managed for the U.S Department of Energy by the University of Chicago Argonne, LLC, under Contract DE-AC-02-06CH11357. This work was authored in part by Los Alamos National Laboratory operated by Triad National Security, LLC, for the U.S. Department of Energy (DOE) under 89233218CNA000001. This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36–08GO28308. References Kort-Kamp WJ, Ferrandon M, Wang X, Park JH, Malla RK, Ahmed T, Holby EF, Myers DJ, Zelenay P. Adaptive learning-driven high-throughput synthesis of oxygen reduction reaction Fe–N–C electrocatalysts. Journal of Power Sources. 2023; 559, 232583. Farghaly AA, Myers DJ, UCHICAGO ARGONNE LLC. Nano-Engineered Catalyst For Improving The Faradaic Efficiency Of Energy Conversion And Electrolysis Systems. United States patent application US 18/653,725. 2024 Nov 21. Onajah S, Sarkar R, Islam MS, Lalley M, Khan K, Demir M, Abdelhamid HN, Farghaly AA. Silica‐Derived Nanostructured Electrode Materials for ORR, OER, HER, CO2RR Electrocatalysis, and Energy Storage Applications: A Review. The Chemical Record. 2024, 24(4), e202300234.

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