ML- Accelerated Automatic Process Exploration Reveals Facile O-Induced Pd Step-Edge Restructuring on Catalytic Time Scales

We combine automatic process exploration with an iteratively trained machine- learning interatomic potential to systematically identify elementary processes occur- ring during the initial oxidation of a Pd step edge. Corresponding process lists are a prerequisite to overcome prevalent predictive-quality microkinetic modeling approaches which consider only a minimum number of hand-selected and thus typically intuitive processes. The exploration readily generates close to 3000 inequivalent elementary processes and thus unveils a complexity far beyond current microkinetic modeling ca- pabilities. Among these processes are numerous low-barrier processes involving the col- lective motion of several atoms that enable a facile O-mediated restructuring of the Pd step edge through the motion of larger PdxOy units. The concomitant interconversion happens on time scales comparable to those of molecular processes of heterogeneous oxidation catalysis. This suggests a dynamic aspect of the operando evolution of the working interface reminiscent of the fluxionality discussed in nanocluster catalysis.