Use high-throughput experimentation combined with genetic algorithms to discover protocols to activate OER catalysts
Alejandro E. Perez-Mendoza a, Corina Andronescu a b, Alexander A. Auer c
a Chemical Technology III, University of Duisburg-Essen, Carl-Benz Straße 199, 47057 Duisburg
b Center for Nanointegration (CENIDE) Duisburg-Essen, 47057 Duisburg, Germany
c Department of Molecular Theory and Spectroscopy, Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470 Mülheim an der Ruhr, Germany
Materials for Sustainable Development Conference (MATSUS)
Proceedings of MATSUS Fall 2024 Conference (MATSUSFall24)
#AMADISTA - Accelerated Materials Discovery Through Automation and Machine Learning
Lausanne, Switzerland, 2024 November 12th - 15th
Organizers: Philippe Schwaller, Tobias Stubhan and Christian Wolff
Poster, Alejandro E. Perez-Mendoza, 360
Publication date: 28th August 2024

Electrochemical conditioning is a widely applied step in the development of stable and active electrocatalyst structures for alkaline oxygen evolution reaction (OER).[1] However, determining the “optimal” conditioning procedure for a specific material is challenging due to the vast number of electrical signals that can be applied, including various potential windows, change rates, and several combinations of reductive and oxidative signals. To address this, we employed a genetic algorithm (GA) for its effectiveness in optimizing multi-criteria problems within non-smooth search spaces, and we combined it with scanning electrochemical cell microscopy (SECCM), a high-throughput technique capable of evaluating thousands of conditioning procedures under similar conditions.[2] Using the capability of these two tools, 20 different starting conditioning procedures were evolved during 10 generations. For each conditioning procedure, the protocol involved the respective electrochemical conditioning and the subsequent OER evaluation to derive activity and stability markers, which were used by the algorithm to generate a new generation of 20 conditioning procedures. This approach aims to identify some common characteristics in the conditioning sequences that significantly “activate” or “deactivate” the nickel-based catalysts for the OER. Understanding the correlation of conditioning techniques, structural evolution of the active sites and their activity may help to identify the key characteristics in the active sites which is useful for the design of electrocatalysts at nanoscale.

C.A. and A.A.A. acknowledge funding by the BMBF in the framework of the PrometH2eus project (03HY105F, 03HY105G). C.A and A.E.P.M. acknowledge funding by Deutsche Forschungs-gemeinschaft (DFG, German Research Foundation; 506711657; SFB 1625) within the collaborative research center 1625 "Atomic-scale understanding and design of multifunctional solid solution surfaces with complex chemical composition”

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