Authors: Ahmed A. Farghaly | Magali Ferrandon | Daniel Schwalbe‐Koda | James Damewood | Jessica Karaguesian | Rafael Gómez‐Bombarelli | Deborah J. Myers
Source: ECS Meeting Abstracts (article, 2022-07-07)
Field: Materials Science — Materials Chemistry

(Invited) Machine Learning Driven Discovery and Optimization of Perovskite Alkaline Electrolyte Oxygen Evolution Reaction Electrocatalysts Electrochemical energy conversion technologies based on the oxygen evolution reaction (OER) are at the heart of many efforts to achieve a sustainable future, carbon-free fuel, and a circular economy. The sluggish kinetics of oxygen electrocatalysis, as well as the high overpotential required to attain practical current densities, limit the efficiency of several promising electrochemical technologies, including water and carbon dioxide electrolyzers, metal–oxygen batteries, and fuel cells. The most efficient OER catalysts are precious metals such as iridium- and ruthenium-based materials (i.e., IrO 2 and RuO 2 ). This fact represents a challenge against the cost-effective implementation of these electrolysis technologies. 1-3 As a result, there is a necessity for the development of cost effective PGM-free OER catalysts, with equivalent or superior activity and durability to the PGM catalysts. This presentation will describe the application of machine learning (ML)-guided materials discovery and high-throughput synthesis to address these concerns, taking advantage of the intriguing properties and rich chemistry of nanoporous materials, the demonstrated capability of machine learning (ML)-guided materials discovery, and the high OER electrocatalytic activity of perovskites especially in alkaline media. 4-10 Simulation of over 8,000 perovskites across a variety of cell sizes, space groups, and compositions using density functional theory (DFT) has been performed. Mining of the simulation data indicated that experimentally-known perovskites are characterized by low energy above the thermodynamic convex hull. Efficient search algorithms, deep learning-based models, and DFT calculations have been used to explore the space of perovskite oxides to produce novel compositions with tailored electronic descriptors. Promising compositions designed for high activity and stability are then selected for high throughput automated synthesis using the High-Throughput Research Facility at Argonne National Laboratory. A correlation between the phase purity, annealing temperature and OER activity has been identified. Acknowledgements This work was supported by the U.S. Department of Energy, Advanced Research Projects Agency-Energy (ARPA-E) under the DIFFERENTIATE program. This work was authored in part by Argonne National Laboratory, a U.S. Department of Energy (DOE) Office of Science laboratory operated for DOE by UChicago Argonne, LLC under contract no. 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Overpotential ; Oxygen evolution ; Electrocatalyst ; Nanoporous ; Materials science ; Electrochemical energy conversion ; Electrolysis of water ; Perovskite (structure) ; Electrolyte ; Catalysis ; Electrochemistry ; Nanotechnology ; Chemical engineering ; Electrolysis ; Chemistry ; Electrode ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Machine Learning and ELM