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

Bhowmik, S and Govind Rajan, A (2022) Chemical vapor deposition of 2D materials: A review of modeling, simulation, and machine learning studies. In: iScience, 25 (3).

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Official URL: https://doi.org/10.1016/j.isci.2022.103832

Abstract

Chemical 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 processes © 2022 The Author(s)

Item Type: Journal Article
Publication: iScience
Publisher: Elsevier Inc.
Additional Information: The copyright for this article belongs to Authors
Department/Centre: Division of Mechanical Sciences > Chemical Engineering
Date Deposited: 16 Mar 2022 06:06
Last Modified: 16 Mar 2022 06:06
URI: http://eprints.iisc.ac.in/id/eprint/71432

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