Molecular Dynamics Simulations for Resolving Scaling Laws of Polyethylene Melts

Enzymatically Activatable Polymers for Disease Diagnosis and Treatment

The irregular expression or activity of enzymes in the human body leads to various pathological disorders and can therefore be used as an intrinsic trigger for more precise identification of disease foci and controlled release of diagnostics and therapeutics, leading to improved diagnostic accuracy, sensitivity, and therapeutic efficacy while reducing systemic toxicity. Advanced synthesis strategies enable the preparation of polymers with enzymatically activatable skeletons or side chains, while understanding enzymatically responsive mechanisms promotes rational incorporation of activatable units and predictions of the release profile of diagnostics and therapeutics, ultimately leading to promising applications in disease diagnosis and treatment with superior biocompatibility and efficiency. By overcoming the challenges, new opportunities will emerge to inspire researchers to develop more efficient, safer, and clinically reliable enzymatically activatable polymeric carriers as well as prodrugs.

Construction of full-atomistic polymer amorphous structures using reverse-mapping from Kremer-Grest models

We propose a method to build full-atomistic (FA) amorphous polymer structures using reverse-mapping from coarse-grained (CG) models. In this method, three models with different resolutions are utilized, namely the CG1, CG2, and FA models. It is assumed that the CG1 model is more abstract than the CG2 model. The CG1 is utilized to equilibrate the system, and then sequential reverse-mapping procedures from the CG1 to the CG2 models and from the CG2 to the FA models are conducted. A mapping relation between the CG1 and the FA models is necessary to generate a polymer structure with a given density and radius of chains. Actually, we have used the Kremer-Grest (KG) model as the CG1 and the monomer-level CG model as the CG2 model. Utilizing the mapping relation, we have developed a scheme that constructs an FA polymer model from the KG model. In the scheme, the KG model, the monomer level CG model, and the FA model are successively constructed. The scheme is applied to polyethylene (PE), cis 1,4-polybutadiene (PB), and poly(methyl methacrylate) (PMMA). As a validation, the structures of PE and PB constructed by the scheme were carefully checked through comparison with those obtained using long-time FA molecular dynamics (MD) simulations. We found that both short- and long-range chain structures constructed by the scheme reproduced those obtained by the FA MD simulations. Then, as an interesting application, the scheme is applied to generate an entangled PMMA structure. The results showed that the scheme provides an efficient and easy way to construct amorphous structures of FA polymers.

MD-GAN with multi-particle input: the machine learning of long-time molecular behavior from short-time MD data

Molecular dynamics simulation is a method of investigating the behavior of molecules, which is useful for analyzing a variety of structural and dynamic properties and mechanisms of phenomena. However, the huge computational cost of large-scale and long-time simulations is an enduring problem that must be addressed. MD-GAN is a machine learning-based method that can evolve part of the system at any time step, accelerating the generation of molecular dynamics data [Endo et al., Proceedings of the AAAI Conference on Artificial Intelligence, 2018, 32]. For the accurate prediction of MD-GAN, sufficient information on the dynamics of a part of the system should be included with the training data. Therefore, the selection of the part of the system is important for efficient learning. In a previous study, only one particle (or vector) of each molecule was extracted as part of the system. The effectiveness of adding information from other particles to the learning process is investigated in this study. When the dynamics of three particles of each molecule were used in the polyethylene experiment, the diffusion was successfully predicted using the training data with a time length of approximately 40%, compared to the single-particle input. Surprisingly, the unobserved transition of diffusion in the training data was also predicted using this method. The reduced cost for the generation of training MD data achieved in this study is useful for accelerating MD-GAN.

Nanoparticle Shape Influence over Poly(lactic acid) Barrier Properties by Molecular Dynamics Simulations

Climate change is leading us to search for new materials that allow a more sustainable environmental situation in the long term. Poly(lactic acid) (PLA) has been proposed as a substitute for traditional plastics due to its high biodegradability. Various components have been added to improve their mechanical, thermal, and barrier properties. The modification of the PLA barrier properties by introducing nanoparticles with different shapes is an important aspect to control the molecular diffusion of oxygen and other gas compounds. In this work, we have described changes in oxygen diffusion by introducing nanoparticles of different shapes through molecular dynamics simulations. Our model illustrates that the existence of curved surfaces and the deposition of PLA around them by short chains generate small holes where oxygen accumulates, forming clusters and reducing their mobility. From the several considered shapes, the sphere is the most suitable structure to improve the barrier properties of the PLA.

Critical Thickness of Free-Standing Nanothin Films Made of Melted Polyethylene Chains via Molecular Dynamics

The mechanical stability of nanothin free-standing films made of melted polyethylene chains was predicted via molecular dynamics simulations in the range of 373.15-673.15 K. The predicted critical thickness, tc, increased with the square of the temperature, T, with additional chains needed as T increased. From T = 373.15 K up to the thermal limit of stability for polyethylene, tc values were in the range of nanothin thicknesses (3.42-5.63 nm), which approximately corresponds to 44-55 chains per 100 nm2. The density at the center of the layer and the interfacial properties studied (density profiles, interfacial thickness, and radius of gyration) showed independence from the film thickness at the same T. The polyethylene layer at its tc showed a lower melting T (<373.15 K) than bulk polyethylene.

Polymer-Based Mechanochromic Composite Material Using Encapsulated Systems

The development of mechanochromic or self-reporting polymers that can indicate damage or fatigue of materials with an optical signal has become of paramount interest to ensure the reliability of the materials and prevent catastrophic failure. This technology can potentially find usefulness for various applications, including in situ monitoring of mechanical events and structural health monitoring systems. An emerging and versatile approach to achieve mechanochromic properties relies on the encapsulation of dye solutions that can be released and activated (chemically or physically) when the walls of the capsules are mechanically damaged. While the mechanochromic effect can be achieved with different types of dyes and operating principles, this framework can also be designed with encapsulating-containers of different shapes and shell materials, such as microcapsules, hollow glass fibers, vascular networks, and micelles, making this concept applicable to a broad range of polymer matrices. An overview of the different encapsulation approaches that have been employed to prepare mechanochromic polymers is given, with a focus on the containers used for this purpose. A brief description of the containers' preparation is provided, and their associated chromic operating principles and progress in their designs are reviewed.

Performance of Coarse Graining in Estimating Polymer Properties: Comparison with the Atomistic Model

Combining atomistic and coarse-grained (CG) models is a promising approach for quantitative prediction of polymer properties. However, the gaps between the length and time scales of atomistic and CG models still need to be bridged. Here, the scale gaps of the atomistic model of polyethylene melts, the bead–spring Kremer–Grest model, and dissipative particle dynamics with the slip-spring model were investigated. A single set of spatial and temporal scaling factors was determined between the atomistic model and each CG model. The results of the CG models were rescaled using the set of scaling factors and compared with those of the atomistic model. For each polymer property, a threshold value indicating the onset of static or dynamic universality of polymers was obtained. The scaling factors also revealed the computational efficiency of each CG model with respect to the atomistic model. The performance of the CG models of polymers was systematically evaluated in terms of both the accuracy and computational efficiency.

A coarse-grain model for entangled polyethylene melts and polyethylene crystallization

The Shinoda-DeVane-Klein (SDK) model is herein demonstrated to be a viable coarse-grain model for performing molecular simulations of polyethylene (PE), affording new opportunities to advance molecular-level, scientific understanding of PE materials and processes. Both structural and dynamical properties of entangled PE melts are captured by the SDK model, which also recovers important aspects of PE crystallization phenomenology. Importantly, the SDK model can be used to represent a variety of materials beyond PE and has a simple functional form, making it unique among coarse-grain PE models. This study expands the suite of tools for studying PE in silico and paves the way for future work probing PE and PE-based composites at the molecular level.

Modeling of Entangled Polymer Diffusion in Melts and Nanocomposites: A Review

This review concerns modeling studies of the fundamental problem of entangled (reptational) homopolymer diffusion in melts and nanocomposite materials in comparison to experiments. In polymer melts, the developed united atom and multibead spring models predict an exponent of the molecular weight dependence to the polymer diffusion very similar to experiments and the tube reptation model. There are rather unexplored parameters that can influence polymer diffusion such as polymer semiflexibility or polydispersity, leading to a different exponent. Models with soft potentials or slip-springs can estimate accurately the tube model predictions in polymer melts enabling us to reach larger length scales and simulate well entangled polymers. However, in polymer nanocomposites, reptational polymer diffusion is more complicated due to nanoparticle fillers size, loading, geometry and polymer-nanoparticle interactions.

Microcapsule-Containing Self-Reporting Polymers

Self-reporting polymers, which can indicate damage or exposure to excessive stress with a clearly perceptible optical signal, are potentially useful for several technological applications, including stress-sensitive sensors that enable in situ monitoring of mechanical events and structural health monitoring systems. A versatile and simple concept to realize this function is the exploitation of microcapsules that are filled with solutions of dyes that are released and chemically or physically activated when the protective shell is damaged. Such microcapsules can readily be incorporated into polymers and the composites thus made can be processed into films, coatings, or other objects. Mechanochromic effects can be realized with different types of dyes and activation schemes. In this concept article, a selection of recent key studies is presented to provide an overview of the state of the field. Different architectures and operating principles and their advantages and drawbacks are reviewed. The parameters that influence the design of microcapsule-based mechanochromic systems are considered and unexplored chromophore systems that might be useful to design future self-reporting polymers are discussed. Finally, specific aspects of capsule design, fabrication, and integration into polymers are presented. Throughout the article, challenges and opportunities of the concept are highlighted and possible future directions are discussed.

Assessing numerical methods for molecular and particle simulation

We discuss the design of state-of-the-art numerical methods for molecular dynamics, focusing on the demands of soft matter simulation, where the purposes include sampling and dynamics calculations both in and out of equilibrium. We discuss the characteristics of different algorithms, including their essential conservation properties, the convergence of averages, and the accuracy of numerical discretizations. Formulations of the equations of motion which are suited to both equilibrium and nonequilibrium simulation include Langevin dynamics, dissipative particle dynamics (DPD), and the more recently proposed "pairwise adaptive Langevin" (PAdL) method, which, like DPD but unlike Langevin dynamics, conserves momentum and better matches the relaxation rate of orientational degrees of freedom. PAdL is easy to code and suitable for a variety of problems in nonequilibrium soft matter modeling; our simulations of polymer melts indicate that this method can also provide dramatic improvements in computational efficiency. Moreover we show that PAdL gives excellent control of the relaxation rate to equilibrium. In the nonequilibrium setting, we further demonstrate that while PAdL allows the recovery of accurate shear viscosities at higher shear rates than are possible using the DPD method at identical timestep, it also outperforms Langevin dynamics in terms of stability and accuracy at higher shear rates.

Onset of static and dynamic universality among molecular models of polymers

A quantitatively accurate prediction of properties for entangled polymers is a long-standing challenge that must be addressed to enable efficient development of these materials. The complex nature of polymers is the fundamental origin of this challenge. Specifically, the chemistry, structure, and dynamics at the atomistic scale affect properties at the meso and macro scales. Therefore, quantitative predictions must start from atomistic molecular dynamics (AMD) simulations. Combined use of atomistic and coarse-grained (CG) models is a promising approach to estimate long-timescale behavior of entangled polymers. However, a systematic coarse-graining is still to be done for bridging the gap of length and time scales while retaining atomistic characteristics. Here we examine the gaps among models, using a generic mapping scheme based on power laws that are closely related to universality in polymer structure and dynamics. The scheme reveals the characteristic length and time for the onset of universality between the vastly different scales of an atomistic model of polyethylene and the bead-spring Kremer-Grest (KG) model. The mapping between CG model of polystyrene and the KG model demonstrates the fast onset of universality, and polymer dynamics up to the subsecond time scale are observed. Thus, quantitatively traceable timescales of polymer MD simulations can be significantly increased.