What Has Been Achieved:
This review aimed 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 discussed 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 have also provided an outlook for the applications of computational approaches to 2D materials synthesis and growth in general.
Importance of Achievement:
With a complete review on the theoretical approaches to modeling the synthesis of 2D materials, our paper pave the way to synthesis by design of 2D materials.
Unique Features of the MIP That Enabled Project:
Access to a unique theoretical and experimental expertise.
K. Momeni, Y. Ji, Y. Wang, S. Paul, S. Neshani, D.E. Yilmaz, Y.K. Shin, D. Zhang, J.-W. Jiang, H.S. Park, S. Sinnott, A. van Duin, V. Crespi, L.-Q. Chen, “Multiscale computational understanding and growth of 2D materials: a review,” npj Computational Materials, 6, 22 (2020). 10.1038/s41524-020-0280-2
Credits/Names: K. Momeni, S. Paul (LA Tech), S. Neshani (IA State), J.-W. Jiang (Shanghai University,), H.S. Park (BU), D.E. Yilmaz, Y.K. Shin, D. Zhang, Y. Ji, Y. Wang, S. Sinnott, A. van Duin, V. Crespi, L.-Q. Chen (PSU)
Year of Research Highlight: 2020
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