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Abstract Recent advancements in the field of two-dimensional (2D) materials have led to the discovery of a wide range of 2D materials with intriguing properties. Atomistic-scale simulation methods have played a key role in these discoveries. In this review, we provide an overview of the recent progress in ReaxFF force field developments and applications in modeling the following layered and nonlayered 2D materials: graphene, transition metal dichalcogenides, MXenes, hexagonal boron nitrides, groups III-, IV- and V-elemental materials, as well as the mixed dimensional van der Waals heterostructures. We further discuss knowledge gaps and challenges associated with synthesis and characterization of 2D materials. We close this review with an outlook addressing the challenges as well as plans regarding ReaxFF development and possible large-scale simulations, which should be helpful to guide experimental studies in a discovery of new materials and devices.
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Multiscale computational understanding and growth of 2D materials: a review
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.
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- Award ID(s):
- 2042683
- PAR ID:
- 10154284
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 6
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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