SKM 2023 – wissenschaftliches Programm
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O: Fachverband Oberflächenphysik
O 96: Solid-Liquid Interfaces III: Reactions and Electrochemistry II
O 96.2: Vortrag
Freitag, 31. März 2023, 10:45–11:00, TRE Phy
Multiscale simulation of nanostructured electrocatalytic systems by coupling neural network surrogates and continuum models — •Younes Hassani Abdollahi1,2, Jürgen Fuhrmann3, and Sebastian Matera1,2 — 1Institut f. Mathematik, Freie Universität Berlin, Berlin, Germany — 2Fritz Haber Institute of the Max Planck Society, Berlin, Germany — 3Weierstraß-Institut f. Angewandte Analysis u. Stochastik, Berlin, Germany
The kinetic Monte Carlo method (kMC) is the physically most sound approach for addressing the kinetic interplay of elementary processes at electrocatalytic surfaces but also comes at high computational costs. Therefore, computationally efficient surrogate models are highly desirable which allow the utilization of kMC simulation results in coarser scale simulations.
Using the oxygen reduction reaction on Pt(111) as a prototypical example, we investigate regression neural networks as surrogates to reproduce the stationary TOF as a function of all reaction conditions, i.e. electrostatic potential, concentrations, and temperature. We found that a relatively shallow network serves as an appropriate choice. This surrogate is then coupled to a conservative and thermodynamically consistent Finite Volume discretization of a nanofluidic model. The resulting hybrid mesoscale model will be employed to discuss the interplay of the nanostructure, transport, and kinetics.