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• W3011220106 abstract "Abstract The successful discovery and isolation of graphene in 2004, and the subsequent synthesis of layered semiconductors and heterostructures beyond graphene have led to the exploding field of two-dimensional (2D) materials that explore their growth, new atomic-scale physics, and potential device applications. This review aims to provide an overview of theoretical, computational, and machine learning methods and tools at multiple length and time scales, and discuss how they can be utilized to assist/guide the design and synthesis of 2D materials beyond graphene. We focus on three methods at different length and time scales as follows: (i) nanoscale atomistic simulations including density functional theory (DFT) calculations and molecular dynamics simulations employing empirical and reactive interatomic potentials; (ii) mesoscale methods such as phase-field method; and (iii) macroscale continuum approaches by coupling thermal and chemical transport equations. We discuss how machine learning can be combined with computation and experiments to understand the correlations between structures and properties of 2D materials, and to guide the discovery of new 2D materials. We will also provide an outlook for the applications of computational approaches to 2D materials synthesis and growth in general." @default.
• W3011220106 created "2020-03-23" @default.
• W3011220106 creator A5006130172 @default.
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• W3011220106 creator A5078240207 @default.
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• W3011220106 creator A5082545877 @default.
• W3011220106 date "2020-03-19" @default.
• W3011220106 modified "2023-10-17" @default.
• W3011220106 title "Multiscale computational understanding and growth of 2D materials: a review" @default.
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