Chemical vapor deposition of 2D materials: A review of modeling, simulation, and machine learning studies

Chemical vapor deposition (CVD) is extensively used to produce large-area twodimensional (2D) materials. Current research is aimed at understanding mechanisms underlying the nucleation and growth of various 2D materials, such as graphene, hexagonal boron nitride (hBN), and transition metal dichalcogenides (e.g., MoS2/WSe2). Herein, we survey the vast literature regarding modeling and simulation of the CVD growth of 2D materials and their heterostructures. We also focus on newer materials, such as silicene, phosphorene, and borophene. We discuss how density functional theory, kinetic Monte Carlo, and reactive molecular dynamics simulations can shed light on the thermodynamics and kinetics of vapor-phase synthesis. We explain how machine learning can be used to develop insights into growth mechanisms and outcomes, as well as outline the open knowledge gaps in the literature. Our work provides consolidated theoretical insights into the CVD growth of 2D materials and presents opportunities for further understanding and improving such processes

Automated summary

Chemical vapor deposition (CVD) is widely used in the production of large-area two-dimensional materials. This article surveys the literature on modeling and simulation of CVD growth of 2D materials, focusing on graphene, hBN, and transition metal dichalcogenides. The article discusses the use of density functional theory, kinetic Monte Carlo, and reactive molecular dynamics simulations to understand the thermodynamics and kinetics of vapor-phase synthesis. It also explores the application of machine learning in studying growth mechanisms and outcomes, and highlights the knowledge gaps in the field.