Dresden 2020 – wissenschaftliches Programm
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O 123.10: Vortrag
Freitag, 20. März 2020, 12:45–13:00, WIL B321
Developing a GAP machine-learned potential for iridium dioxide nanoparticles — •Jakob Timmermann, Christoph Scheurer, and Karsten Reuter — Technische Universität München
Iridium dioxide is currently the preferred material for highly active, yet chemically stable nanoparticle catalysts enabling the electrochemical oxygen evolution reaction (OER) in proton exchange membrane electrolyzers. Full ab initio molecular dynamics (MD) simulations of the reactive processes at the electrified nanoparticle surface would be highly desirable for mechanistic catalyst improvement, but are computationally not tractable for a foreseeable time. To overcome the limitations regarding system size and propagation time, MDs based on machine-learned interatomic potentials are an appealing alternative.
Here, we present a corresponding Gaussian Approximation Potential (GAP) for IrO2 combining two-body and smooth overlap of atomic positions (SOAP) descriptors to capture the atomic environment. The potential is trained with density-functional theory (DFT) data comprising IrO2 bulk, various surface slabs, Wulff shape nanoparticles, as well as semi-amorphous structures iteratively obtained from short MD trajectories based on the developing GAP. The final GAP is found to faithfully provide a wide range of static geometric and energetic key parameters. MD simulations based on this GAP now provide first insight into stability and special OER active sites offered by nanoparticles of varying size and shape.