Implementing Reactivity in Molecular Dynamics Simulations with the Interface Force Field (IFF-R) and Other Harmonic Force Fields

Winetrout, Jordan J.; Kanhaiya, Krishan; Sachdeva, Geeta; Pandey, Ravindra Mohan; Damirchi, Behzad; Duin, Adri van; Odegard, Gregory M.; Heinz, Hendrik

doi:10.48550/arxiv.2107.14418

Cited by 13 publications

(22 citation statements)

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“…Recently, a modified version of IFF has been developed to allow the simulation of bond dissociation. 24 This so-called reactive interface force field (IFF-R) uses Morse potentials to describe covalent bond interactions instead of traditional fixed-bond harmonic potentials. Thus, IFF-R can effectively simulate covalent bond disassociation associated with large mechanical deformations.…”

Section: Introductionmentioning

confidence: 99%

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Molecular Dynamics Modeling of Epoxy Resins Using the Reactive Interface Force Field

et al. 2021

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Predictive computational modeling of polymer materials is necessary for the efficient design of composite materials and the corresponding processing methods. Molecular dynamics (MD) modeling is especially important for establishing accurate processing-structure-property relationships for neat resins. For MD modeling of amorphous polymer materials, an accurate force field is fundamental to reliable prediction of properties. Reactive force fields, in which chemical bonds can be formed or broken, offer further capability in predicting the mechanical behavior of amorphous polymers subjected to relatively large deformations. To this end, the Reactive Interface Force Field (IFF-R) has been recently developed to provide an efficient means to predict the behavior of materials under these conditions. Although IFF-R has been proven to be accurate for some crystalline organic and inorganic systems, it has not yet been proven to be accurate for amorphous polymer systems. The objective of this study is to use IFF-R to predict the thermomechanical properties of three different epoxy systems and validate with experimental measurements. The results indicate that IFF-R predicts thermo-mechanical properties that agree closely with experiment. Therefore, IFF-R can be used to reliably establish mechanical properties of polymers on the molecular level for future design of new composite materials and processing methods.

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Section: Introductionmentioning

confidence: 99%

“…IFF-R has been proven to work well for predicting the physical and mechanical properties of carbon nanotubes and crystalline polyacrylonitrile, cellulose, and FCC iron. 24 However, it has not yet been explored for high-performance thermosetting resins with an amorphous molecular structure.…”

Section: Introductionmentioning

confidence: 99%

Molecular Dynamics Modeling of Epoxy Resins Using the Reactive Interface Force Field

et al. 2021

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“…The MD simulations were performed using the LAMMPS (large-scale atomic/molecular massively parallel simulator) MD software package. The IFF-R 16 was used to describe interatomic forces, as implemented in LAMMPS, 17,18 using the parametrization described by Odegard et al 19 The IFF-R is a reactive force field which is a modified version of the polymer consistent force field−interface force field (PCFF−IFF), 20,21 with the harmonic covalent bond stretch potentials replaced with Morse potentials. The use of Morse potentials allows for the more accurate calculation of bond stretch energies at higher deformations, including the possibility of bond dissociation.…”

Section: Molecular Modelingmentioning

confidence: 99%

Understanding the Origin of the Low Cure Shrinkage of Polybenzoxazine Resin by Computational Simulation

Gaikwad

Krieg

Deshpande

et al. 2021

ACS Appl. Polym. Mater.

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Thermoset resin-based composite materials are widely used in the aerospace industry, mainly because of their high stiffness-to-weight and strength-to-weight ratios. A major issue with the use of thermoset resins in fiber composites is processinduced residual stresses that are formed from resin chemical shrinkage during the curing process. These residual stresses within the composite material ultimately result in reduced durability and residual deformations of the final product. Polybenzoxazine (PBZ) polymer resins have demonstrated near-zero volumetric shrinkage during the curing process. Although the low shrinkage of PBZ is promising in terms of reduced process-induced residual stresses, little is known about the physical causes. In this work, molecular dynamics (MD) simulations are performed with a reactive force field to predict the physical properties (gelation point, evolution of network, mass density, and volumetric shrinkage) and mechanical properties (bulk modulus, shear modulus, Young's modulus, Poisson's ratio, and yield strength) as a function of the cross-linking density and thermal properties (glass transition temperature). The MD modeling procedure is validated herein using experimental measurements of the modeled PBZ resin. The results of this study are used to provide a physical understanding of the zero-shrinkage phenomenon of PBZ. This information is also a critical input to future process modeling efforts for PBZ composites.

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“…The advantages of these techniques include insight at the scale of atoms, access to the large nanometer scale, and the ability to simulate entire stress-strain experiments from equilibrium to failure in high accuracy. [6] [7] Simulations then allow to design and screen a larger number of model structures with a variety of defects and nanoscale features of interest, inspired by experimental data and theory, and examine the failure mechanisms in depth. However, although more efficient than experiments, the computational cost is still considerable.…”

Section: Introductionmentioning

confidence: 99%

“…We train our HS-GNN on initial configurations of about 1000 independent CNTs. Tensile properties used as ground truth in training set are generated from their stress-strain curves of complex carbon nanostructures using molecular dynamics simulations with the thoroughly validated IFF force field that includes bond breaking (IFF-R) [42,43,7]. The resulting HS-GNN can predict Young's modulus and strength for new atomic arrangements (not used in training set) with high accuracy (5-10% MSE error), including larger graphitic structures.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Carbon Nanostructure Mechanical Properties and Role of Defects Using Machine Learning

Zhao¹,

Winetrout²,

Xu³

et al. 2021

Preprint

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Carbon fiber and graphene-based nanostructures such as carbon nanotubes (CNTs) and defective structures have extraordinary potential as strong and lightweight materials. A longstanding bottleneck has been lack of understanding and implementation of atomic-scale engineering to harness the theoretical limits of modulus and tensile strength, of which only a fraction is routinely reached today. Here we demonstrate accurate and fast predictions of mechanical properties for CNTs and arbitrary 3D graphitic assemblies based on a training set of over 1000 stress-strain curves from cutting-edge reactive MD simulation and machine learning (ML). Several ML methods are compared and show that our newly proposed hierarchically structured graph neural networks with spatial information (HS-GNNs) achieve predictions in modulus and strength for any 3D nanostructure with only 5-10% error across a wide range of possible values. The reliability is sufficient for practical applications and a great improvement over off-the shelf ML methods with up to 50% deviation, as well as over earlier models for specific chemistry with 20% deviation. The algorithms allow more than 10 times faster mechanical property predictions than traditional molecular dynamics simulations, the identification of the role of defects and random 3D morphology, and high-throughput screening of 3D structures for enhanced mechanical properties. The algorithms can potentially be scaled to morphologies up to 100 nm in size, expanded for chemically similar compounds, and trained to predict a broader range of properties.

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Implementing Reactivity in Molecular Dynamics Simulations with the Interface Force Field (IFF-R) and Other Harmonic Force Fields

Cited by 13 publications

References 0 publications

Molecular Dynamics Modeling of Epoxy Resins Using the Reactive Interface Force Field

Molecular Dynamics Modeling of Epoxy Resins Using the Reactive Interface Force Field

Understanding the Origin of the Low Cure Shrinkage of Polybenzoxazine Resin by Computational Simulation

Prediction of Carbon Nanostructure Mechanical Properties and Role of Defects Using Machine Learning

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