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O: Fachverband Oberflächenphysik

O 78: Heterogeneous Catalysis and Surface Dynamics II

O 78.4: Vortrag

Donnerstag, 30. März 2023, 11:15–11:30, TRE Phy

Exploration of IrO2 electrocatalyst deactivation via machine learning potentials — •Hao Wan, Hendrik Heenen, Simon Wengert, Christoph Scheurer, and Karsten Reuter — Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany

IrO2 catalysts are one of the best performing oxygen evolution reaction (OER) catalysts in terms of both catalytic activity and stability under harsh acidic conditions. Yet, they still suffer from deactivation under long term OER conditions due to possible changes in surface compositions and morphology. To shed light into these deactivation processes, characterization of the microscopic structure and composition of IrO2 interfaces is necessary. The involved phase space, however, is vast and its exploration requires extensive sampling and automatic (surface) structure searches which are unfeasible via Density Functional Theory (DFT) calculations. Enabling the intensive computations, machine learning interatomic potentials (MLIP) retain DFT accuracy to within a few meV per atom while reducing the computational cost by up to three orders of magnitude.

In this contribution, we present a reliable MLIP based on Gaussian approximation potentials to investigate IrO2 deactivation. Stable and metastable IrO2 surfaces at various surface oxidation states have been extensively sampled using parallel tempering. Relevant surfaces are suggested by evaluating their relative stability compared to rutile-IrO2(110), while considering electrochemical conditions. OER activity from these surfaces can be estimated using established reaction descriptors which will help unravel likely deactivation mechanisms.