Atomistic insights into the oxidation of flat and stepped platinum surfaces using large-scale machine learning potential-based grand-canonical monte carlo

Understanding catalyst surface structure changes under reactive conditions has become an important topic with the increasing interest in operando measurement and modeling. In this work, we develop a workflow to build machine learning potentials (MLPs) for simulating complicated chemical systems with large spatial and time scales, in which the committee model strategy equips the MLP with uncertainty estimation, enabling the active learning protocol. The methods are applied to constructing PtOx MLP based on explored configurations from bulk oxides to amorphous oxidized surfaces, which cover most ordered high-oxygen-coverage platinum surfaces within an accessible energy range. This MLP is used to perform large-scale grand canonical Monte Carlo simulations to track detailed structure changes during oxidations of flat and stepped Pt surfaces, which is normally inaccessible to costly ab initio calculations. These structural evolution trajectories reveal the stages of surface oxidation without laborious manual construction of surface models. We identify the building blocks of oxide formation and elucidate the surface oxide formation mechanism on Pt surfaces. The insightful interpretations would deeply help us understand the oxide formation on other metal surfaces. We demonstrate that these large-scale simulations would be a powerful tool to investigate realistic structures and the formation mechanisms of complicated systems.