Presentation Information
[1C13]Promotion of Water-Gas Shift Reaction on Cu Clusters on Cu(111) via Machine Learning Force Fields and Microkinetic Modeling with Lateral Interaction
*Muhammad Fadhlan Anshor1, Harry Handoko Halim1, Yoshitada Morikawa1 (1. Department of Precision Engineering, Graduate School of Engineering, The University of Osaka)
We investigate the impact of Cu clustering on the water-gas shift reaction (WGSR) using machine learning force fields (MLFF) and microkinetic modeling with lateral interactions. A Gaussian Approximation Potential was trained and validated on DFT data, enabling efficient NEB and vibrational analyses. Medium to high coverage CO surface structures were obtained via minima hopping to include coverage-dependent effects. Microkinetic modeling with CatMAP shows that Cu clusters exhibit up to 3 orders of magnitude higher TOF than Cu(111), mainly due to lower water dissociation barriers. Lateral interactions have a minor effect on Cu(111) but significantly influence activity on clusters like Cu7. Including these interactions brings TOF predictions in line with experimental values. This MLFF-based framework allows accurate modeling of complex catalytic systems.