Dynamic aspects of graphene deformation and fracture from approximate density functional theory

Jung, Gang Seob; Irle, Stephan; Sumpter, Bobby G.

doi:10.1016/j.carbon.2022.01.002

Cited by 8 publications

(7 citation statements)

References 45 publications

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“…A schematic of the current study is shown in figure 1(a). In a previous study [37], we showed that DFTB could correctly predict both mechanical behaviors and lattice deformation of graphene under various loading conditions, in good agreement with DFT calculations. Therefore, we here utilized DFTB to generate extensive data for possible fracture scenarios of graphene under various loadings by mixing two tensile and one shear deformation.…”

Section: Introductionsupporting

confidence: 69%

“…Also, the NNP is very flexible in capturing non-linear deformation-stress behaviors well (figure 5), which is challenging with the fixed functional form, such as harmonic equations in classical forcefields. In the previous study, we observed that it is challenging to match both deformation and non-linearity of stress-strain behaviors of graphene simultaneously with those from DFT even through reactive forcefields [37]. Crack dynamics is one of the exciting applications for NNPs due to its intrinsic multiscale feature.…”

Section: Discussionmentioning

confidence: 99%

“…A recent in-situ TEM with reactive molecular simulation has shown that lattice distortion at the crack tip in 2D materials, WS 2 with a hexagonal lattice, can drastically change the entire path of crack propagation [35]. Also, theoretical comparisons between DFT and AIREBO show that the lattice distortion under tensile loading is significantly different [37]. Therefore, we hypothesized that developing an NNP of graphene for lattice deformation and fracture under various loadings could provide a more faithful prediction of crack propagation in graphene, especially along the AC direction.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial neural network potentials for mechanics and fracture dynamics of two-dimensional crystals ^**

Jung

Myung

Irle

2023

Mach. Learn.: Sci. Technol.

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Understanding the mechanics and failure of materials at the nanoscale is critical for their engineering and applications. The accurate atomistic modeling of brittle failure with crack propagation in covalent crystals requires a quantum mechanics-based description of individual bond-breaking events. Artificial neural network potentials (NNPs) have emerged to overcome the traditional, physics-based modeling tradeoff between accuracy and accessible time and length scales. Previous studies have shown successful applications of NNPs for describing the structure and dynamics of molecular systems and amorphous or liquid phases of materials. However, their application to deformation and failure processes in materials is still uncommon. In this study, we discuss the apparent limitations of NNPs for the description of deformation and fracture under loadings and propose a way to generate and select training data for their employment in simulations of deformation and fracture simulations of crystals. We applied the proposed approach to 2D crystalline graphene, utilizing the density-functional tight-binding (DFTB) method for more efficient and extensive data generation in place of density functional theory (DFT). Then, we explored how the data selection affects the accuracy of the developed artificial NNPs. It revealed that NNP’s reliability should not only be measured based on the total energy and atomic force comparisons for reference structures but also utilize comparisons for physical properties, e.g., stress-strain curves and geometric deformation. In sharp contrast to popular reactive bond order potentials, our optimized NNP predicts straight crack propagation in graphene along both armchair and zigzag lattice directions, as well as higher fracture toughness of zigzag edge direction. Our study provides significant insight into crack propagation mechanisms on atomic scales and highlights strategies for NNP developments of broader materials.

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Section: Introductionsupporting

confidence: 69%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial neural network potentials for mechanics and fracture dynamics of two-dimensional crystals ^**

Jung

Myung

Irle

2023

Mach. Learn.: Sci. Technol.

Self Cite

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“…40 The interaction between atoms was described with the density functional tight binding (DFTB) method through the DFTB+ package, 41 utilizing a previously developed interface for LAMMPS. 42 We employed the 3OB 43 parameters with the DFTB3 scheme. The initial structure was relaxed at 10 K for 2000 steps through the Berendsen thermostat with a time step of 0.5 fs.…”

Section: Methodsmentioning

confidence: 99%

Active learning of neural network potentials for rare events

Jung,

Choi,

Lee

2024

Digital Discovery

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“…In the present work, the tensile behavior of GA is analyzed at two temperatures, 300 and 1000 K, and thus the limitation of the AIREBO potential for the simulation of such effects needs to be discussed. In [68], ReaxFF [69] and AIREBO have been utilized to understand the deformation and fracture mechanics of graphene at the atomic scale to compare the results from DFTB. The other important factor that affects the obtained results is the simulation technique for tension.…”

Section: Simulation Detailsmentioning

confidence: 99%

Strength and Deformation Behavior of Graphene Aerogel of Different Morphologies

Baimova,

Shcherbinin

2023

Materials

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Graphene aerogels are of high interest nowadays since they have ultralow density, rich porosity, high deformability, and good adsorption. In the present work, three different morphologies of graphene aerogels with a honeycomb-like structure are considered. The strength and deformation behavior of these graphene honeycomb structures are studied by molecular dynamics simulation. The effect of structural morphology on the stability of graphene aerogel is discussed. It is shown that structural changes significantly depend on the structural morphology and the loading direction. The deformation of the re-entrant honeycomb is similar to the deformation of a conventional honeycomb due to the opening of the honeycomb cells. At the first deformation stage, no stress increase is observed due to the structural transformation. Further, stress concentration on the junctions of the honeycomb structure and over the walls occurs. The addition of carbon nanotubes and graphene flakes into the cells of graphene aerogel does not result in a strength increase. The mechanisms of weakening are analyzed in detail. The obtained results further contribute to the understanding of the microscopic deformation mechanisms of graphene aerogels and their design for various applications.

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Dynamic aspects of graphene deformation and fracture from approximate density functional theory

Cited by 8 publications

References 45 publications

Artificial neural network potentials for mechanics and fracture dynamics of two-dimensional crystals ^**

Artificial neural network potentials for mechanics and fracture dynamics of two-dimensional crystals ^**

Active learning of neural network potentials for rare events

Strength and Deformation Behavior of Graphene Aerogel of Different Morphologies

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Dynamic aspects of graphene deformation and fracture from approximate density functional theory

Cited by 8 publications

References 45 publications

Artificial neural network potentials for mechanics and fracture dynamics of two-dimensional crystals **

Artificial neural network potentials for mechanics and fracture dynamics of two-dimensional crystals **

Active learning of neural network potentials for rare events

Strength and Deformation Behavior of Graphene Aerogel of Different Morphologies

Contact Info

Product

Resources

About

Artificial neural network potentials for mechanics and fracture dynamics of two-dimensional crystals ^**

Artificial neural network potentials for mechanics and fracture dynamics of two-dimensional crystals ^**