Modeling Atomic Structure of Platinum Deposition on Graphene with Machine Learning Interatomic Potentials

Depositing catalytic metals, such as Platinum, in forms like monolayers, sub-monolayers, or nanoparticles onto two-dimensional substrates like graphene, enhances surface area and reduces material usage, resulting in significant cost savings. Computational modeling of these catalytic systems is crucial for predicitng their reactivity. However, many computational investigations, primarily based on First-Principles methods like Density Functional Theory (DFT), often rely on arbitrarily assumed crystal configurations of metal clusters on graphene for computational tractability. Given that DFT electronic and chemical properties heavily depend on the underlying atomic configurations, it is imperative to meticulously study the atomic structure of these metal depositions before making property predictions with DFT. Recent advancements in machine learning interatomic potentials have shown considerable promise in enabling large-scale simulations while achieving accuracy comparable to DFT at a fraction of the computational cost. In this study, we employ neural network potentials to conduct extensive Monte Carlo simulations to investigate Platinum growth on graphene. Our results provide a comprehensive analysis of the competition between lateral and vertical growth of Platinum under a wide range of loadings and demonstrate excellent agreement with experimental measurements conducted using transmission electron microscopy.