A molecular dynamics investigation of the planar elongational rheology of chemically identical dendrimer-linear polymer blends

Hajizadeh, Elnaz; Todd, B. D.; Daivis, Peter J.

doi:10.1063/1.4919654

Cited by 14 publications

(6 citation statements)

References 44 publications

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“…Still, the great power of MD is its proficiency to predict microstructure dynamics along its deterministic trajectory at an atomistic level. Applications of MD in the field of polymeric materials include topics such as macromolecular dynamics [119,120,121,122,123,124], intercalation phenomena in polymer/clay nanocomposites [63], structure of interfaces [125,126,127], polymer membranes [128,129], crystal structures [130,131,132], diffusion phenomena [133,134,135,136], segregation phenomena [137], tribological properties and crack propagation [138,139,140], thin films and surfaces [141,142,143,144], liquid crystalline polymers [145,146], rheology of polymeric systems [147,148,149,150], application of elongational flows on polymers using nonequilibrium MD [151,152], and the simulations of reactive systems such as crosslinking and decomposition of polymers using the ReaxFF force field [153,154,155,156].…”

Section: Simulation Methodsmentioning

confidence: 99%

A Review of Multiscale Computational Methods in Polymeric Materials

2017

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Polymeric materials display distinguished characteristics which stem from the interplay of phenomena at various length and time scales. Further development of polymer systems critically relies on a comprehensive understanding of the fundamentals of their hierarchical structure and behaviors. As such, the inherent multiscale nature of polymer systems is only reflected by a multiscale analysis which accounts for all important mechanisms. Since multiscale modelling is a rapidly growing multidisciplinary field, the emerging possibilities and challenges can be of a truly diverse nature. The present review attempts to provide a rather comprehensive overview of the recent developments in the field of multiscale modelling and simulation of polymeric materials. In order to understand the characteristics of the building blocks of multiscale methods, first a brief review of some significant computational methods at individual length and time scales is provided. These methods cover quantum mechanical scale, atomistic domain (Monte Carlo and molecular dynamics), mesoscopic scale (Brownian dynamics, dissipative particle dynamics, and lattice Boltzmann method), and finally macroscopic realm (finite element and volume methods). Afterwards, different prescriptions to envelope these methods in a multiscale strategy are discussed in details. Sequential, concurrent, and adaptive resolution schemes are presented along with the latest updates and ongoing challenges in research. In sequential methods, various systematic coarse-graining and backmapping approaches are addressed. For the concurrent strategy, we aimed to introduce the fundamentals and significant methods including the handshaking concept, energy-based, and force-based coupling approaches. Although such methods are very popular in metals and carbon nanomaterials, their use in polymeric materials is still limited. We have illustrated their applications in polymer science by several examples hoping for raising attention towards the existing possibilities. The relatively new adaptive resolution schemes are then covered including their advantages and shortcomings. Finally, some novel ideas in order to extend the reaches of atomistic techniques are reviewed. We conclude the review by outlining the existing challenges and possibilities for future research.

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Section: Simulation Methodsmentioning

confidence: 99%

A Review of Multiscale Computational Methods in Polymeric Materials

2017

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“…This is important in tribology since PEF occurs in the entrance region of high pressure, elastohydrodynamic contacts [95]. PEF simulations have been widely used to study polymers flow [96,97] but more rarely for lubricant-sized molecules. Baig et al [98] studied the rheological and structural properties of linear liquid n-alkanes (C 10 , C 12 , C 20 ) using NEMD simulations under PEF.…”

Section: Modified Nemd Algorithmsmentioning

confidence: 99%

Advances in nonequilibrium molecular dynamics simulations of lubricants and additives

2018

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Nonequilibrium molecular dynamics (NEMD) simulations have provided unique insights into the nanoscale behaviour of lubricants under shear. This review discusses the early history of NEMD and its progression from a tool to corroborate theories of the liquid state, to an instrument that can directly evaluate important fluid properties, towards a potential design tool in tribology. The key methodological advances which have allowed this evolution are also highlighted. This is followed by a summary of bulk and confined NEMD simulations of liquid lubricants and lubricant additives, as they have progressed from simple atomic fluids to ever more complex, realistic molecules. The future outlook of NEMD in tribology, including the inclusion of chemical reactivity for additives, and coupling to continuum methods for large systems, is also briefly discussed.

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“…The significant role of frequency-dependent viscoelastic moduli of the polyurethane matrix on the acoustic performance of these coatings underpins the critical need for establishment of the linkages between polyurethane chemistry (molecular scale) and bulk morphology (mesoscopic scale) to that of the macroscopic viscoelastic behavior [42]. These could be achieved through integrating the molecular dynamic simulations [43,44,45,46] and self-consistent field theory [47] calculations into the continuum models in a unified multiscale materials informatics framework. In addition, to assess the performance of coatings under the operational conditions, the effect of hydrostatic pressure (i.e., operation depth) is being studied and will be the subject of future paper.…”

Section: Discussionmentioning

confidence: 99%

A machine learning accelerated inverse design of underwater acoustic polyurethane coatings with cylindrical voids

Weeratunge¹,

Shireen²,

Sagar³

et al. 2022

Preprint

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Here, we report the development of a detailed "Materials Informatics" framework for the design of acoustic coatings for underwater sound attenuation through integrating Machine Learning (ML) and statistical optimization algorithms with a Finite Element Model (FEM). The finite element models were developed to simulate the realistic performance of the acoustic coatings based on polyurethane (PU) elastomers with embedded cylindrical voids. The FEM results revealed that the frequency-dependent viscoelastic behavior of the polyurethane matrix has a significant impact on the magnitude and frequency of the absorption peak associated with the cylinders at low frequencies, which has been commonly ignored in previous studies on similar systems. The data generated from the FEM was used to train a Deep Neural Network (DNN) to accelerate the design process, and subsequently, was integrated with a Genetic Algorithm (GA) to determine the optimal geometric parameters of the cylinders to achieve maximized, broadband, low-frequency waterborne sound attenuation. A significant, broadband, low-frequency attenuation is achieved by optimally configuring the layers of cylindrical voids and using attenuation mechanisms, including Fabry-Pérot resonance and Bragg scattering of the layers of voids. Integration of the machine learning technique into the optimization algorithm further accelerated the exploration of the high dimensional design space for the targeted performance. The developed DNN exhibited significantly increased speed (by a factor of 4.5 × 10 3 ) in predicting the absorption coefficient compared to the conventional FEM(s). Therefore, the acceleration brought by the materials informatics framework brings a paradigm shift to the design and development of acoustic coatings compared to the conventional trial-and-error practices.

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A molecular dynamics investigation of the planar elongational rheology of chemically identical dendrimer-linear polymer blends

Cited by 14 publications

References 44 publications

A Review of Multiscale Computational Methods in Polymeric Materials

A Review of Multiscale Computational Methods in Polymeric Materials

Advances in nonequilibrium molecular dynamics simulations of lubricants and additives

A machine learning accelerated inverse design of underwater acoustic polyurethane coatings with cylindrical voids

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