Computational Synthesis of MoS<sub>2</sub> Layers by Reactive Molecular Dynamics Simulations: Initial Sulfidation of MoO<sub>3</sub> Surfaces

Hong, Sungwook; Krishnamoorthy, Aravind; Rajak, Pankaj; Tiwari, Subodh; Misawa, Masanaru; Shimojo, Fuyuki; Kalia, Rajiv K.; Nakano, Aiichiro; Vashishta, Priya

doi:10.1021/acs.nanolett.7b01727

Cited by 62 publications

(68 citation statements)

References 44 publications

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“…As shown in Fig. 2, RMD simulations using the initial force-field parameters for Mo/O/S/H elements (previously published by Hong et al 35 and recently extended to H 2 O/H 2 S systems) qualitatively describe the overall trends in the numbers of H-S bonds ( Fig. 2a), Mo-S bonds ( Fig.…”

Section: Multiobjective Genetic Algorithm For Optimizing Reaxff Parammentioning

confidence: 54%

Multiobjective genetic training and uncertainty quantification of reactive force fields

et al. 2018

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The ReaxFF reactive force-field approach has significantly extended the applicability of reactive molecular dynamics simulations to a wide range of material properties and processes. ReaxFF parameters are commonly trained to fit a predefined set of quantummechanical data, but it remains uncertain how accurately the quantities of interest are described when applied to complex chemical reactions. Here, we present a dynamic approach based on multiobjective genetic algorithm for the training of ReaxFF parameters and uncertainty quantification of simulated quantities of interest. ReaxFF parameters are trained by directly fitting reactive molecular dynamics trajectories against quantum molecular dynamics trajectories on the fly, where the Pareto optimal front for the multiple quantities of interest provides an ensemble of ReaxFF models for uncertainty quantification. Our in situ multiobjective genetic algorithm workflow achieves scalability by eliminating the file I/O bottleneck using interprocess communications. The in situ multiobjective genetic algorithm workflow has been applied to high-temperature sulfidation of MoO 3 by H 2 S precursor, which is an essential reaction step for chemical vapor deposition synthesis of MoS 2 layers. Our work suggests a new reactive molecular dynamics simulation approach for far-from-equilibrium chemical processes, which quantitatively reproduces quantum molecular dynamics simulations while providing error bars.

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Section: Multiobjective Genetic Algorithm For Optimizing Reaxff Parammentioning

confidence: 54%

Multiobjective genetic training and uncertainty quantification of reactive force fields

et al. 2018

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“…The COMB3 potentials have been used to simulate the experimental CVD deposition and growth of graphene and metal on substrates 98 . The ReaxFF potentials have been used to explore the structures of several 2D materials under intercalation, study the defects and groups on the surfaces of MXene materials, simulate CVD growth of MoS 2 layers, and explore the synthesis of 2D polymeric materials in experiments 7,99 . In general, COMB3 and ReaxFF can further explore 2D materials, as their functional forms can describe heterogeneous systems.…”

Section: Molecular Dynamicsmentioning

confidence: 99%

“…The critical challenge for developing theoretical and computational design tools for the synthesis of 2D materials is the broad range of length and temporal scales involved in their growth process. For example, it may require quantum mechanical and atomistic reactive force-field calculations to determine the activation energies for atomic migration on a surface 6 and understand the atomistic surface reaction mechanisms 7 , and then a finite element method (FEM) to model the mesoscale mass transport phenomena 8 . Other challenges include incorporating the effects of substrates including the types of substrate defects 9 , the possible wrinkling of 2D films 10 , the effect of van der Waals (vdW) interactions at the mesoscale 11 , and the growth kinetics unique to atomically thin materials 12 .…”

Section: Introductionmentioning

confidence: 99%

Multiscale computational understanding and growth of 2D materials: a review

et al. 2020

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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.npj Computational Materials (2020) 6:22 ; https://doi.

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“…ReaxFF is parameterized exclusively from quantum chemical calculations, and has been shown to reproduce the potential energy surfaces, structures, and reaction barriers for reactive systems at nearly the accuracy of quantum chemical methods but at costs nearly as low as conventional force fields. Applications of ReaxFF have been reported for a wide range of functional materials, including energetic materials, anodes and electrolytes for fuel cell and battery technologies, metals and metal oxides, metal organic frameworks 2D materials, among others.…”

Section: Large Scale Methods/molecular Dynamicsmentioning

confidence: 99%

Modeling Diffusion in Functional Materials: From Density Functional Theory to Artificial Intelligence

Elbaz

Furman

Toroker

2019

Adv Funct Materials

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Diffusion describes the stochastic motion of particles and is often a key factor in determining the functionality of materials. Modeling diffusion of atoms can be very challenging for heterogeneous systems with high energy barriers. In this report, popular computational methodologies are covered to study diffusion mechanisms that are widely used in the community and both their strengths and weaknesses are presented. In static approaches, such as electronic structure theory, diffusion mechanisms are usually analyzed within the nudged elastic band (NEB) framework on the ground electronic surface usually obtained from a density functional theory (DFT) calculation. Another common approach to study diffusion mechanisms is based on molecular dynamics (MD) where the equations of motion are solved for every time step for all the atoms in the system. Unfortunately, both the static and dynamic approaches have inherent limitations that restrict the classes of diffusive systems that can be efficiently treated. Such limitations could be remedied by exploiting recent advances in artificial intelligence and machine learning techniques. Here, the most promising approaches in this emerging field for modeling diffusion are reported. It is believed that these knowledge-intensive methods have a bright future ahead for the study of diffusion mechanisms in advanced functional materials.observed in liquids, gases, and solid-state materials. One of the main cornerstones for the macroscopic understanding of diffusion was laid down in the mid-19th century by Adolf Fick. By observing Graham's experiments in gases and salt water, and by the analogy between Fourier's law of thermal conduction and diffusion, Fick developed two equations known as the Fick's laws , , , J D C x y z t

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Computational Synthesis of MoS₂ Layers by Reactive Molecular Dynamics Simulations: Initial Sulfidation of MoO₃ Surfaces

Cited by 62 publications

References 44 publications

Multiobjective genetic training and uncertainty quantification of reactive force fields

Multiobjective genetic training and uncertainty quantification of reactive force fields

Multiscale computational understanding and growth of 2D materials: a review

Modeling Diffusion in Functional Materials: From Density Functional Theory to Artificial Intelligence

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Computational Synthesis of MoS2 Layers by Reactive Molecular Dynamics Simulations: Initial Sulfidation of MoO3 Surfaces

Cited by 62 publications

References 44 publications

Multiobjective genetic training and uncertainty quantification of reactive force fields

Multiobjective genetic training and uncertainty quantification of reactive force fields

Multiscale computational understanding and growth of 2D materials: a review

Modeling Diffusion in Functional Materials: From Density Functional Theory to Artificial Intelligence

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Computational Synthesis of MoS₂ Layers by Reactive Molecular Dynamics Simulations: Initial Sulfidation of MoO₃ Surfaces