Machine Learning Driven Discovery and Optimization of Multi-Component Oxygen Evolution Reaction Catalysts

Abstract

Electrochemical energy conversion and storage technologies are in the heart of the societal pursuit towards circular economy, greenhouse gas emission-free fuel and sustainable energy future. Oxygen evolution reaction (OER) plays a central role in many of these electrochemical technologies such as electrolyzers and metal-air batteries. However, the sluggish kinetics of 4-electron water oxidation to O2, the resulting large overpotential and severe energy loss limit the development of energy efficient devices. The low-abundance, prohibitive cost, and moderate stability of state-of-the-art OER catalysts, iridium- and ruthenium-based materials i.e. IrO2 and RuO2, also limit the cost effective implementation of electrolyzers.1-3 This presentation will describe our research 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 We will describe our work in simulating over 8,000 perovskites across a variety of cell sizes, space groups, and compositions using density functional theory (DFT). By mining this simulation data, we found that experimentally-known perovskites have low energy above the thermodynamic convex hull. Then, using efficient search algorithms, deep learning-based models, and DFT calculations, the space of perovskite oxides is explored 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.AcknowledgementsThis 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. DE-AC02-06CH11357.References Katsounaros, Ioannis, Serhiy Cherevko, Aleksandar R. Zeradjanin, and Karl JJ Mayrhofer. "Oxygen electrochemistry as a cornerstone for sustainable energy conversion." Angewandte Chemie International Edition53, no. 1 (2014): 102-121.Lee, Youngmin, Jin Suntivich, Kevin J. May, Erin E. Perry, and Yang Shao-Horn. "Synthesis and activities of rutile IrO2 and RuO2 nanoparticles for oxygen evolution in acid and alkaline solutions." The journal of physical chemistry letters3, no. 3 (2012): 399-404.Cherevko, S. et al. Oxygen and hydrogen evolution reactions on Ru, RuO2, Ir, and IrO2 thin film electrodes in acidic and alkaline electrolytes: A comparative study on activity and stability. Today 262, 170–180 (2016).Nahar, Lamia, Ahmed A. Farghaly, Richard J. Alan Esteves, and Indika U. Arachchige. "Shape controlled synthesis of Au/Ag/Pd nanoalloys and their oxidation-induced self-assembly into electrocatalytically active aerogel monoliths." Chemistry of Materials29, no. 18 (2017): 7704-7715.Farghaly, Ahmed A., Rezaul K. Khan, and Maryanne M. Collinson. "Biofouling-resistant platinum bimetallic alloys." ACS applied materials & interfaces10, no. 25 (2018): 21103-21112.Khan, Rezaul K., Ahmed A. Farghaly, Tiago A. Silva, Dexian Ye, and Maryanne M. Collinson. "Gold-Nanoparticle-Decorated Titanium Nitride Electrodes Prepared by Glancing-Angle Deposition for Sensing Applications." ACS Applied Nano Materials2, no. 3 (2019): 1562-1569.Farghaly, Ahmed A., Mai Lam, Christopher J. Freeman, Badharinadh Uppalapati, and Maryanne M. Collinson. "Potentiometric measurements in biofouling solutions: comparison of nanoporous gold to planar gold." Journal of The Electrochemical Society163, no. 4 (2015): H3083.Suntivich, Jin, Kevin J. May, Hubert A. Gasteiger, John B. Goodenough, and Yang Shao-Horn. "A perovskite oxide optimized for oxygen evolution catalysis from molecular orbital principles." Science334, no. 6061 (2011): 1383-1385.Hwang, Jonathan, Zhenxing Feng, Nenian Charles, Xiao Renshaw Wang, Dongkyu Lee, Kelsey A. Stoerzinger, Sokseiha Muy et al. "Tuning perovskite oxides by strain: electronic structure, properties, and functions in (electro) catalysis and ferroelectricity." Materials Today31 (2019): 100-118.Gómez-Bombarelli, Rafael, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Alán Aspuru-Guzik. "Automatic chemical design using a data-driven continuous representation of molecules." ACS central science4, no. 2 (2018): 268-276.