Machine learning of reaction pathways in chemical vapor deposition of MoS₂ monolayers

Scalable synthesis of two dimensional (2D) materials is a major bottleneck to more widespread adoption of layered material-based devices. Chemical vapor deposition (CVD) has emerged as a viable method for large-scale synthesis of 2D materials. However, neither experiment nor theory has been able to decipher mechanisms and selection rules, or make predictions of optimized growth parameters. Experimental challenges stem from the use of probes like TEM to characterize CVD growth reactions in situ under elevated temperatures and pressures. Computational synthesis, which simulates CVD processes using reactive molecular dynamics methods provides the atomistic resolution necessary for the deduction of reaction mechanisms. Here we use neural networks trained on trajectories from several hundred simulations of computational synthesis of MoS₂ monolayers to uncover the dependence of product stoichiometry, crystallinity and phase distribution on reaction parameters like temperature, sulfur and hydrogen partial pressures, thus paving the way for rational design of CVD synthesis techniques.