Local chemistry-guided molecular beam epitaxy growth of snse on mgo via combined reaxff modeling and machine learning

Abstract

Subtle variations in local growth chemistry can fundamentally alter the morphology of layered chalcogenide thin films, yet the atomic-scale mechanisms underlying this sensitivity remain poorly understood. Here, we combine molecular beam epitaxy experiments, reactive molecular dynamics simulations, and machine learning analysis to establish a chemistry–structure relationship for SnSe thin film growth on MgO. Experimentally, we show that modifying the Sn:Se flux ratio and deposition sequence switches SnSe growth from three-dimensional island formation to laterally coalesced smooth films, accompanied by a pronounced reduction in surface roughness. Reactive molecular dynamics simulations reveal that this transition originates from an intrinsic chemical asymmetry between Sn and Se that alters adatom surface diffusion and aggregation behavior during growth. Se-rich environments stabilize surface passivation and promote lateral growth, whereas Sn-rich conditions favor vertical aggregation through rapid Sn clustering. To independently assess morphology trends without imposing physical assumptions, we apply an unsupervised machine learning framework to images rendered from the simulation snapshots. The resulting low-dimensional representation of surface morphologies recovers the same chemistry-dependent roughness trends observed experimentally and in simulations, providing data-driven validation of the underlying atomistic mechanisms. Because nanoscale roughness strongly influences carrier scattering, phonon transport, and domain stability in SnSe-based electronic and energy devices, these results provide a chemistry-guided framework for morphology control in layered chalcogenide thin films.