Molecular dynamics of polyisoprene/polystyrene oligomer blends: The role of self-concentration and fluctuations on blend dynamics

The effect of chemical constitution on polyisoprene dynamics

Polyisoprene (PI) melts have been studied, with most reports focusing on systems with high 1,4-cis content. In contrast, 1,4-trans PI homopolymers or random copolymers have seldom been examined, despite a handful of investigations suggesting a distinct dynamic behavior. Herein, we employ all-atom simulations to investigate the effect of chemical architecture on the dynamics of cis and trans-PI homopolymers, as well as copolymers. We examine the thermodynamic, conformational, and structural properties of the polymers and validate the performance of the models. We probe chain dynamics, revealing that cis-PI presents accelerated translation and reorientation modes relative to trans as recorded by the mean square displacement of the chain center-of-mass as well as by the characteristic times of the lower modes in a Rouse analysis. Interestingly, progressing to higher modes, we observe a reversal with trans units exhibiting faster dynamics. This was further confirmed by calculations of local carbon-hydrogen vector reorientation dynamics, which offer a microscopic view of segmental mobility. To obtain insight into the simulation trajectories, we evaluate the intermediate incoherent scattering function that supports a temperature-dependent crossover in relative mobility that extends over separations beyond the Kuhn-length level. Finally, we analyzed the role of non-Gaussian displacements, which demonstrate that cis-PI exhibits increased heterogeneity in dynamics over short-timescales in contrast to trans-PI, where deviations persist over times extending to terminal dynamics. Our all-atom simulations provide a fundamental understanding of PI dynamics and the impact of microstructure while providing important data for the design and optimization of PI-based materials.

Deep convolutional neural networks for generating atomistic configurations of multi-component macromolecules from coarse-grained models

Despite the modern advances in the available computational resources, the length and time scales of the physical systems that can be studied in full atomic detail, via molecular simulations, are still limited. To overcome such limitations, coarse-grained (CG) models have been developed to reduce the dimensionality of the physical system under study. However, to study such systems at the atomic level, it is necessary to re-introduce the atomistic details into the CG description. Such an ill-posed mathematical problem is typically treated via numerical algorithms, which need to balance accuracy, efficiency, and general applicability. Here, we introduce an efficient and versatile method for backmapping multi-component CG macromolecules of arbitrary microstructures. By utilizing deep learning algorithms, we train a convolutional neural network to learn structural correlations between polymer configurations at the atomistic and their corresponding CG descriptions, obtained from atomistic simulations. The trained model is then utilized to get predictions of atomistic structures from input CG configurations. As an illustrative example, we apply the convolutional neural network to polybutadiene copolymers of various microstructures, in which each monomer microstructure (i.e., cis-1,4, trans-1,4, and vinyl-1,2) is represented as a different CG particle type. The proposed methodology is transferable over molecular weight and various microstructures. Moreover, starting from a specific single CG configuration with a given microstructure, we show that by modifying its chemistry (i.e., CG particle types), we are able to obtain a set of well equilibrated polymer configurations of different microstructures (chemistry) than the one of the original CG configuration.

Accelerated Diffusion Following Deprotection in Chemically Amplified Resists

Polymeric chemically amplified resists (CARs) are critical materials for high-throughput lithographic processes. A photoactivated acid-anion catalyst changes the polymer's solubility via a deprotection reaction, which enables pattern development through selective dissolution. To capture observed reaction kinetics, reaction-diffusion models employ a catalyst diffusivity that is accelerated by reaction. However, the microscopic origin and factors contributing to this phenomena remain unclear. Herein, we employ detailed atomistic molecular dynamics simulations to examine the impact of protecting group removal and material relaxation on catalyst mobility. We report data on polymer density, catalyst dispersion, excess free volume, and segmental dynamics with increasing time/extent of deprotection. We then propose simple kinetic Monte Carlo algorithms that can describe both molecular dynamics simulations of deprotection reactions and experimental data.

Spatially and Temporally Resolved Heterogeneities in a Miscible Polymer Blend

Mapping the spatial and temporal heterogeneities in miscible polymer blends is critical for understanding and further improving their material properties. However, a complete picture on the heterogeneous dynamics is often obscured in ensemble measurements. Herein, the spatial and temporal heterogeneities in fully miscible polystyrene/oligostyrene blend films are investigated by monitoring the rotational diffusion of embedded individual probe molecules using defocused wide-field fluorescence microscopy. In the same blend film, three significantly different types of dynamical behaviors (referred to as modes) of the probe molecules can be observed at the same time, namely, immobile, continuously rotating, and intermittently rotating probe molecules. This reveals a prominent spatial heterogeneity in local dynamics at the nanometer scale. In addition to that, temporal heterogeneity is uncovered by the nonexponential characteristic of the rotational autocorrelation functions of single-molecule probes. Moreover, the occurrence probabilities of these different modes strongly depend on the polystyrene: oligostyrene ratios in the blend films. Remarkably, some probe molecules switch between the continuous and intermittent rotational modes at elevated temperature, indicating a possible alteration in local dynamics that is triggered by the dynamic heterogeneity in the blends. Although some of these findings can be discussed by the self-concentration model and the results provided by ensemble averaging techniques (e.g., dielectric spectroscopy), there are implications that go beyond current models of blend dynamics.

Spatio-temporal heterogeneities in nanosegregated single-molecule polymeric nanoparticles

The study of the coupling between structural and dynamical heterogeneities in nanostructured systems is essential for the design of hybrid materials with the desired properties. Here, we use atomistic molecular dynamics simulations to closely examine the dynamical heterogeneities in nanostructured single-molecule nanoparticles consisting of mikto-arm star copolymers with poly(ethylene oxide), PEO, and polystyrene, PS, arms. The particles exhibit an internally nanostructured morphology, resembling either "Janus-like" or "patchy-like" morphology when the functionality of the stars varies. The differences in the local environment result in strong intramolecular dynamical heterogeneities. In the proximity of the star core, geometric constraints promote unfavorable PEO:PS contacts that lead to a behavior similar to dynamically asymmetric miscible polymer blends or disordered copolymers. In contrast, further away from the core, the nanosegregation induces segmental dynamics very similar to the one found in the homopolymer star analogues.

Coarse-graining of polyisoprene melts using inverse Monte Carlo and local density potentials

Bottom-up coarse-graining of polymers is commonly performed by matching structural order parameters such as distribution of bond lengths, bending and dihedral angles, and pair distribution functions. In this study, we introduce the distribution of nearest-neighbors as an additional order parameter in the concept of local density potentials. We describe how the inverse-Monte Carlo method provides a framework for forcefield development that is capable of overcoming challenges associated with the parameterization of interaction terms in polymer systems. The technique is applied on polyisoprene melts as a prototype system. We demonstrate that while different forcefields can be developed that perform equally in terms of matching target distributions, the inclusion of nearest-neighbors provides a straightforward route to match both thermodynamic and conformational properties. We find that several temperature state points can also be addressed, provided that the forcefield is refined accordingly. Finally, we examine both the single-particle and the collective dynamics of the coarse-grain models, demonstrating that all forcefields present a similar acceleration relative to the atomistic systems.

Molecular dynamics study of natural rubber-fullerene composites: connecting microscopic properties to macroscopic behavior

Macroscopic and microscopic properties of fullerene (C₆₀)-cis-polyisoprene (cis-PI) composites at varying fullerene concentrations were investigated using atomistic molecular dynamics (MD) simulations over microsecond time scales. Results show that the introduction of fullerenes into a polymer matrix increases density, bulk modulus and heat capacity while thermal expansivity decreases. The presence of fullerenes slowed the diffusion of both C₆₀ and cis-PI. Moreover, increasing fullerene concentration results in ordering of the cis-PI chains at the cis-PI-fullerene interfaces and shrinking of bulk PI regions. Free energy calculations of fullerene dimerization suggest that fullerenes disperse at low and aggregate at high fullerene concentrations. Our multi-scaled analysis approach demonstrates the role of 'ordered' regions adjacent to the interface between cis-PI and fullerene in controlling the level of order and mobility of the cis-PI chains. The relationship between the microscopic behavior and the changes in mechanical and thermal properties are discussed. Our study is beneficial for further studies and development of advanced rubber technology for novel, cost-effective, material with very high stiffness and thermal endurance with optimizing conditions of filler contents.

Response of adhesive polymer interfaces to repeated mechanical loading and the spatial variation of diffusion coefficient and stresses in a deforming polymer film

Comprehensive molecular simulations are conducted to show that polymer crosslinks preserve the strength of solid-polymer (melt) interfaces when they are subjected to repeated mechanical loading. The spatial variation of the diffusion coefficient and local stresses is also investigated along the polymer thickness, during deformation. After each loading cycle, a reduction in entanglement strength is observed at the fracture site. The work of adhesion also decreases over consecutive loading cycles, when fracture is induced at the same site. Reduction in both, the work of adhesion and the entanglement strength, decreases as the crosslink density increases. Diffusion coefficient and stresses vary significantly and in a complex manner along the film thickness during the entire deformation process. These variations were due to peculiar configurations occurring at each instance of separation, which are analyzed and explained in this work. The variation of diffusion coefficient during deformation suggests that other dynamic properties, such as viscosity, also vary spatially during polymer deformation.

Multiscale Molecular Simulations of Polymer-Matrix Nanocomposites: or What Molecular Simulations Have Taught us About the Fascinating Nanoworld

Following the substantial progress in molecular simulations of polymer-matrix nanocomposites, now is the time to reconsider this topic from a critical point of view. A comprehensive survey is reported herein providing an overview of classical molecular simulations, reviewing their major achievements in modeling polymer matrix nanocomposites, and identifying several open challenges. Molecular simulations at multiple length and time scales, working hand-in-hand with sensitive experiments, have enhanced our understanding of how nanofillers alter the structure, dynamics, thermodynamics, rheology and mechanical properties of the surrounding polymer matrices.