Computational Understanding of the Growth of 2D Materials. Issue 11 (19th August 2018)

• Record Type: Journal Article
• Authors: Gao, Junfeng; Xu, Ziwei; Chen, Shuai; Bharathi, Madurai S.; Zhang, Yong‐Wei
• Is Part Of: Advanced theory and simulations. Volume 1:Issue 11(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-08-19
• Subjects: 2D materials -- computation -- growth; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.201800085
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract: Over the last two decades, remarkable progress has been made in use of computational methods for understanding 2D materials growth. The aim of this Review is to provide an overview of several state‐of‐the‐art computational methods for the modelling and simulation of 2D materials growth. First, the current status of 2D materials, and their major growth methods are addressed. Next, the applications of the ab initio method in 2D materials growth is discussed, focusing on reaction of precursors, diffusion of adatoms, energetics and kinetics of growth fronts, and effects of substrates. Then, the applications of the molecular dynamics approach in 2D materials growth is discussed, with emphasis on the growth of graphene on various substrates and the growth of boron nitride and silicene. Furthermore, the applications of the kinetic Monte Carlo method in 2D materials growth are discussed. The parametrization of the method and its application in dimer distribution, and nonlinear edge growth of graphene are discussed. Subsequently, the applications of the phase‐field method in 2D materials growth are discussed, focusing on the growth rate and morphological evolution of 2D domains. Finally, perspectives and conclusions are presented. Abstract : Great efforts have been made using computational modelling to understand the mechanisms of 2D materials growth . The gained understandings have been proven to be useful for guiding the growth of large‐area, high‐quality 2D materials. In this Review, an overview of extensive case studies is presented using computational modelling to gain in‐depth understandings of 2D materials growth.