Prof. Karsten Reuter
Fritz-Haber-Institut der Max-Planck-Gesellschaft
Faradayweg 4-6, D-14195 Berlin (Germany)
Electrocatalytic processes gain an ever-increasing importance for a future sustainable energy system. Electrolytic generation of hydrogen, electroreduction of CO2 to synthetic fuels or the reverse fuel cell processes are key components for the required storage, transport and provision of energy on a global scale. Unfortunately, the transition to corresponding energy technologies is still largely impeded by insufficient efficiencies or stabilities of hitherto employed materials or devices. Many of these limitations arise at the involved solid-liquid interfaces, which often undergo strong structural and compositional changes in the operating device. Such operando evolution presents already a severe challenge to the predictive-quality modeling and simulation of working thermal catalysts [1]. In interfacial electrocatalysis, this is further aggravated by the simultaneous need to reliably capture solvent dynamics and the long-range electrostatics in the diffuse double layer. The present state-of-the-art is therefore largely characterized by harsh approximations. Operando evolution is generally not treated, solvation effects are often ignored and the applied bias at best considered through thermodynamic potentials. In my talk I will introduce this context and survey our recent work toward a more realistic first-principles description of electrocatalytic processes at solid-liquid interfaces. In particular I will discuss a fully grand-canonical approach employing implicit solvation to account for capacitive charging [2] and the use of machine-learned interatomic potentials for extensive searches of the interfacial structures formed operando [3].
[1] First-Principles Based Multiscale Modeling of Heterogeneous Catalysis,
A. Bruix, J.T. Margraf, M. Andersen, and K. Reuter, Nature Catal. 2, 659 (2019).
[2] Electrosorption at Metal Surfaces from First Principles,
N.G. Hoermann, N. Marzari, and K. Reuter, npj Comp. Mat. 6, 136 (2020).
[3] IrO2 Surface Complexions Identified Through Machine Learning and Surface Investigations,
J. Timmermann, F. Kraushofer, N. Resch, Z. Mao, M. Riva, Y. Lee, C. Staacke, M. Schmid,
C. Scheurer, G. Parkinson, U. Diebold, and K. Reuter, Phys. Rev. Lett. 125, 206101 (2020).