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  1. Ferritic-martensitic steels, such as T91, are candidate materials for high-temperature applications, including superheaters, heat exchangers, and advanced nuclear reactors. Considering these alloys’ wide applications, an atomistic understanding of the underlying mechanisms responsible for their excellent mechano-chemical properties is crucial. Here, we developed a modified embedded-atom method (MEAM) potential for the Fe-Cr-Si-Mo quaternary alloy system—i.e., four major elements of T91—using a multi-objective optimization approach to fit thermomechanical properties reported using density functional theory (DFT) calculations and experimental measurements. Elastic constants calculated using the proposed potential for binary interactions agreed well with ab initio calculations. Furthermore, the computed thermal expansion and self-diffusion coefficients employing this potential are in good agreement with other studies. This potential will offer insightful atomistic knowledge to design alloys for use in harsh environments. 
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    Free, publicly-accessible full text available April 1, 2024
  2. Transition metal dichalcogenides (TMDCs) are potential materials for future optoelectronic devices. Grain boundaries (GBs) can significantly influence the optoelectronic properties of TMDC materials. Here, we have investigated the mechanical characteristics of tungsten diselenide (WSe 2 ) monolayers and failure process with symmetric tilt GBs using ReaxFF molecular dynamics simulations. In particular, the effects of topological defects, loading rates, and temperatures are investigated. We considered nine different grain boundary structures of monolayer WSe 2 , of which six are armchair (AC) tilt structures, and the remaining three are zigzag (ZZ) tilt structures. Our results indicate that both tensile strength and fracture strain of WSe 2 with symmetric tilt GBs decrease as the temperature increases. We revealed an interfacial phase transition for high-angle GBs reduces the elastic strain energy within the interface at finite temperatures. Furthermore, brittle cracking is the dominant failure mode in the WSe 2 monolayer with tilted GBs. WSe 2 GB structures showed more strain rate sensitivity at high temperatures than at low temperatures. 
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  3. Abstract

    Reproducible wafer-scale growth of two-dimensional (2D) materials using the Chemical Vapor Deposition (CVD) process with precise control over their properties is challenging due to a lack of understanding of the growth mechanisms spanning over several length scales and sensitivity of the synthesis to subtle changes in growth conditions. A multiscale computational framework coupling Computational Fluid Dynamics (CFD), Phase-Field (PF), and reactive Molecular Dynamics (MD) was developed – called the CPM model – and experimentally verified. Correlation between theoretical predictions and thorough experimental measurements for a Metal-Organic CVD (MOCVD)-grown WSe2model material revealed the full power of this computational approach. Large-area uniform 2D materials are synthesized via MOCVD, guided by computational analyses. The developed computational framework provides the foundation for guiding the synthesis of wafer-scale 2D materials with precise control over the coverage, morphology, and properties, a critical capability for fabricating electronic, optoelectronic, and quantum computing devices.

     
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  4. 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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