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O: Fachverband Oberflächenphysik

O 78: Heterogeneous Catalysis and Surface Dynamics II

O 78.3: Vortrag

Donnerstag, 30. März 2023, 11:00–11:15, TRE Phy

Machine-learning Gaussian Approximation Potentials to discover RuO₂ surface reconstructions — •Yonghyuk Lee, Jakob Timmermann, Christoph Scheurer, and Karsten Reuter — Fritz-Haber-Institut der MPG, Berlin, Germany

Machine-learning Gaussian Approximation Potentials (GAPs) have recently evolved into a powerful class of surrogate models for computationally demanding first-principles calculations. Combined with structure exploration techniques, they enable us to examine the potential energy surface of interest with a hitherto unforeseen combination of physical accuracy and computational efficiency and to achieve global surface structure determination (SSD) for increasingly complex systems. This can be exploited, e.g., to discover novel surface motifs, which are critical in understanding the dynamics of heterogeneous catalysts under operating conditions. In our preceding study on IrO₂, this methodology was extended by a general and data-efficient active-learning framework that allows for the on-the-fly generation of GAPs via the actual surface exploration process. During the iterative GAP refinement for RuO₂, we have now identified plenty of unknown low-energy reconstructions of RuO₂ low index facets. Intriguingly, by extending the searching space to larger surface unit cells, we discovered c(2×2) reconstructions of RuO₂(100), which provide solutions to longstanding questions in heterogeneous catalysis and experiments.

[1] J. Timmermann et al., Phys. Rev. Lett. 125, 206101 (2020)

[2] J. Timmermann et al., J. Chem. Phys., 155, 244107 (2021)