Reparameterization of the REBO-CHO potential for graphene oxide molecular dynamics simulations

Fonseca, Alexandre F.; Lee, Geunsik; Borders, Tammie L.; Zhang, Hengji; Kemper, Travis; Shan, Tzu Ray; Sinnott, Susan B.; Cho, Kyeongjae

doi:10.1103/physrevb.84.075460

Cited by 36 publications

(30 citation statements)

References 50 publications

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“…On the purely empirical side, restricting our discussion to the more transferable methods, Baskes and co-workers have developed the embedded atom method (EAM 65,66 ), which-opposite to ReaxFF-was mainly formulated for metals, yet has since been modified (MEAM 67 ) to treat oxides, hydrides and hydrocarbons. Furthermore, the bondorder concept, as initiated by Abell, 68 Tersoff, 69,70 and Brenner, 71 was further developed into the AIREBO method 72 by Stuart, Tutein and Harrison, as well as into the highly transferable COMB method [73][74][75][76][77] by Sinnott, Philpott, and co-workers. We refer readers to recent reviews for more in-depth comparisons of empirical reactive methods, 73,74 and of simulation methods for large-scale molecular dynamics on reactive systems.…”

Section: Current Reaxff Methodologymentioning

confidence: 99%

The ReaxFF reactive force-field: development, applications and future directions

et al. 2016

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The reactive force-field (ReaxFF) interatomic potential is a powerful computational tool for exploring, developing and optimizing material properties. Methods based on the principles of quantum mechanics (QM), while offering valuable theoretical guidance at the electronic level, are often too computationally intense for simulations that consider the full dynamic evolution of a system. Alternatively, empirical interatomic potentials that are based on classical principles require significantly fewer computational resources, which enables simulations to better describe dynamic processes over longer timeframes and on larger scales. Such methods, however, typically require a predefined connectivity between atoms, precluding simulations that involve reactive events. The ReaxFF method was developed to help bridge this gap. Approaching the gap from the classical side, ReaxFF casts the empirical interatomic potential within a bond-order formalism, thus implicitly describing chemical bonding without expensive QM calculations. This article provides an overview of the development, application, and future directions of the ReaxFF method. INTRODUCTIONAtomistic-scale computational techniques provide a powerful means for exploring, developing and optimizing promising properties of novel materials. Simulation methods based on quantum mechanics (QM) have grown in popularity over recent decades due to the development of user-friendly software packages making QM level calculations widely accessible. Such availability has proved particularly relevant to material design, where QM frequently serves as a theoretical guide and screening tool. Unfortunately, the computational cost inherent to QM level calculations severely limits simulation scales. This limitation often excludes QM methods from considering the dynamic evolution of a system, thus hampering our theoretical understanding of key factors affecting the overall behaviour of a material. To alleviate this issue, QM structure and energy data are used to train empirical force fields that require significantly fewer computational resources, thereby enabling simulations to better describe dynamic processes. Such empirical methods, including reactive force-field (ReaxFF), 1 trade accuracy for lower computational expense, making it possible to reach simulation scales that are orders of magnitude beyond what is tractable for QM.Atomistic force-field methods utilise empirically determined interatomic potentials to calculate system energy as a function of atomic positions. Classical approximations are well suited for nonreactive interactions, such as angle-strain represented by harmonic potentials, dispersion represented by van der Waals potentials and Coulombic interactions represented by various polarisation schemes. However, such descriptions are inadequate for modelling changes in atom connectivity (i.e., for modelling chemical reactions as bonds break and form). This motivates the

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Section: Current Reaxff Methodologymentioning

confidence: 99%

The ReaxFF reactive force-field: development, applications and future directions

et al. 2016

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“…Some recent work overcoming this limitation has extended the AIREBO force field to include hydroxyl groups (by considering bonded interactions). However, the (non-bonded) hydrogen bond interactions in that model were not fully validated [20]. Since the hydrogen bonding energy (, 6.2 kcal/mol for our model) is much smaller than the covalent bond (, 43 kcal/mol for the C -O bond), the rupture and reforming of the hydrogen bond dominates the material behaviour, and as such is a critical model property.…”

Section: Resultsmentioning

confidence: 98%

Bioinspired design of functionalised graphene

Qin

Buehler

2012

Molecular Simulation

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Functionalised graphene is an attractive candidate for novel applications in the fabrication of nanodevices or novel composites. Here, we apply molecular dynamics simulations to investigate the assembly of functionalised graphene ribbons and sheets. We illustrate that by designing the location and density of functional groups, the material self-assembles into a defined stable folded structure with lower energy and mechanical properties distinct from the pristine graphene. We show that the hydrogen bonds formed between the functional groups are crucial for this folding process, similar to the driving forces of assembly in many biological protein materials. We propose that such functionalised graphene materials could be employed to realise the bottom-up design of structural materials with tunable mechanical properties as they are expected to achieve multiple mechanical functions under varied conditions.

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“…The reactive term in the name indicates that this potential has the capacity to model bond breaking and bond formation. The second-generation REBO potential (REBO2) 15 provided even more accurate descriptions of short-range bonding in solid state carbon materials and hydrocarbon systems, and was subsequently further extended to C-H-O, [16][17][18] C-H-F, 19 and C-H-S 20 systems. Most recently, Liang et al 21 parameterized REBO2 to model the metallic, ionic and covalent bonding present in Mo-S systems.…”

Section: Introductionmentioning

confidence: 99%

Charge Transfer Potentials

Cheng

Liang

Sinnott

2013

Computational Catalysis

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The last two decades have witnessed a dramatic rise in computational resources that has facilitated tremendous progress in computational science. In particular, this progress has enabled the application of quantum-based methods such as Hartree-Fock (HF) theory and density functional theory (DFT) to compute the potential energy surfaces of numerous complex reactions that are critical to understanding catalytic reactions. These approaches provide high fidelity because of their explicit treatment of electronic structure; however, their computational cost increases rapidly with system size. Therefore, they are limited to a relatively small number of atoms (o500). To overcome this limitation, classical empirical methods (also known as interatomic potentials) that model molecules and materials at the atomic scale without explicitly treating electrons have been developed and have been employed in molecular dynamics (MD) and Monte Carlo (MC) simulations. Such simulations have been employed to examine catalysis at length and time scales beyond the reach of quantum-based approaches.The main strength of classical empirical potentials is their low computational cost relative to electronic-structure calculations. Recently, systems with billions of atoms have been modeled in MD simulations.

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Reparameterization of the REBO-CHO potential for graphene oxide molecular dynamics simulations

Cited by 36 publications

References 50 publications

The ReaxFF reactive force-field: development, applications and future directions

The ReaxFF reactive force-field: development, applications and future directions

Bioinspired design of functionalised graphene

Charge Transfer Potentials

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