Machine Learning-Guided Discovery of PGM-Free Oxyphosphide Oxygen Evolution Catalysts for Alkaline Water Electrolysis
Authors: Farghaly Ahmed A. , Onajah Sammy , Lalley Marja , Kort-Kamp Wilton , Li Sirui , Kreider Melissa E , Alia Shaun M , Yu Haoran , Myers Deborah J. 
Published: December 2024

The sluggish kinetics of the oxygen evolution reaction (OER) create a bottleneck for many sustainable energy technologies, including water electrolysis, metal-air batteries, and electrochemical CO2 reduction reaction (CO2RR). While platinum group metal (PGM) catalysts like IrO2 and RuO2 exhibit high OER activity in proton exchange membrane water electrolysis (PEMWE), their cost hinders widespread adoption. 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, bridging the gap and 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, the vast chemical space of oxyphosphide compositions makes traditional trial-and-error approaches to catalyst discovery inefficient. This presentation explores the application of machine learning (ML) to accelerate the discovery of novel PGM-free oxyphosphide OER catalysts for alkaline water electrolysis.

Recent advancements in active learning, a powerful branch of machine learning, have demonstrated efficient and effective explorations of materials synthesis space, particularly for electrocatalytic applications. For instance, active learning has been used to optimize synthesis conditions for oxygen reduction reaction (ORR) catalysts, leading to a 33% improvement in activity compared to initial samples [1-4]. This presentation proposes a similar active learning strategy to explore and optimize the synthesis space for PGM-free oxyphosphide OER catalysts.

This presentation will detail the following:

Challenges of Traditional Approaches

Active Learning for OER Catalyst Design

Machine Learning and High-Throughput Synthesis

This combined approach offers a powerful strategy for the rapid discovery of efficient and cost-effective PGM-free oxyphosphide OER catalysts, ultimately accelerating advancements in sustainable water electrolysis technologies.

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.

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