文章基本信息

标题：Chemical vapor deposition of 2D materials: A review of modeling, simulation, and machine learning studies
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作者：Sayan Bhowmik ; Ananth Govind Rajan
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2022
卷号：25
期号：3
页码：1-32
DOI：10.1016/j.isci.2022.103832
语种：English
出版社：Elsevier
摘要：SummaryChemical vapor deposition (CVD) is extensively used to produce large-area two-dimensional (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 processesGraphical abstractDisplay OmittedMaterials synthesis; Nanomaterials; Theoretical chemistry