Machine learning insight into h-BN growth on Pt(111) from atomic states

Abstract

The growth of monolayer h-BN from boron and nitrogen atoms on Pt(111) is investigated using molecular dynamics combined with machine-learning potentials trained based on first-principles data. An h-BN monolayer is formed by depositing boron and nitrogen atoms at a constant temperature. In the growth process, a Y-shaped node is formed around boron, which then forms a quadrangular ring that transforms into the hexagonal ring comprising the basic unit of h-BN. In these processes, Pt atoms emerge from the substrate and contribute to fusion of the BN clusters. These phenomena appear to be characteristic of h-BN formation on Pt(111). Both increase of the number of deposited nitrogen and cooling of the substrate significantly improve the quality of formed h-BN. This suggests that the formation of high-quality h-BN on Pt(111) is a combination of surface-mediated growth and boron precipitation due to cooling.