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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. [ABSTRACT FROM AUTHOR]

Vinyl-addition polynorbornenes are candidates for designing high-performance polymers due to unique characteristics, which include a high glass transition temperature associated with a rigid backbone. Recent studies have established that the processability and properties of these polymers can be fine-tuned by using targeted substitutions. However, synthesis with different catalysts results in materials with distinct properties, potentially due to the presence of various stereoisomers that are difficult to quantify experimentally. Herein, we develop all-atom models of polynorbornene oligomers based on classical force fields and density functional theory. To establish the relationship between chemical architecture, chain conformations, and melt structure, we perform detailed molecular dynamics simulations with the fine-tuned atomistic force field and propose simpler coarse-grained descriptions to address the high molecular weight limit. All-atom simulations of oligomers suggest high glass transition temperatures in the range of 550–600 K. In the melt state (800 K), meso chains form highly rigid extended coils ( C ∞ ≈ 11 ) with amorphous structural characteristics similar to the X-ray diffraction data observed in the literature. In contrast, simulations with racemo chains predict highly helical tubular chain conformations that could promote assembly into crystalline structures. [ABSTRACT FROM AUTHOR]

Statistical learning is employed to present a principled framework for the establishment of quantitative structure‐property relationships (QSPR). Property predictions of industrial polymers formed by multiple reagents and at varying molecular weights are focused. A theoretical description of QSPR as well as a rigorous mathematical method is developed for the assimilation of experimental data. Results show that these methods can perform exceptionally well at establishing QSPR for glass transition temperature and intrinsic viscosity of polyesters. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Multiscale modeling of polymers exchanges information between coarse and fine representations of molecules to capture material properties over a wide range of spatial and temporal scales. Restoring details at a finer scale requires us to generate information following embedded physics and statistics of the models at two different levels of description. Techniques designed to address this persistent challenge balance among accuracy, efficiency, and general applicability. In this work, we present an image-based approach for structural backmapping from coarse-grained to atomistic models with cis-1,4 polyisoprene melts as an illustrative example. Through machine learning, we train conditional generative adversarial networks on the correspondence between configurations at the levels considered. The trained model is subsequently applied to provide predictions of atomistic structures from the input coarse-grained configurations. The effect of different data representation schemes on training and prediction quality is examined. Our proposed backmapping approach shows remarkable efficiency and transferability over different molecular weights in the melt based on training sets constructed from oligomeric compounds. We anticipate that this versatile backmapping approach can be readily extended to other complex systems to provide high-fidelity initial configurations with minimal human intervention. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The protective properties of trehalose on cholestrol-containing lipid dipalmitoylphosphatidylcholine (DPPC) are studied through molecular simulations. Such a phenomenon suggests that trehalose can inhibit changes in lateral organization of the bilayer components, which is vital in preventing leakage during preservation procedures.

The local dynamics and the conformational properties of polyisoprene next to a smooth graphite surface constructed by graphene layers are studied by a multiscale methodology. First, fully atomistic molecular dynamics simulations of oligomers next to the surface are performed. Subsequently, Monte Carlo simulations of a systematically derived coarse-grained model generate numerous uncorrelated structures for polymer systems. A new reverse backmapping strategy is presented that reintroduces atomistic detail. Finally, multiple extensive fully atomistic simulations with large systems of long macromolecules are employed to examine local dynamics in proximity to graphite. Polyisoprene repeat units arrange close to a parallel configuration with chains exhibiting a distribution of contact lengths. Efficient Monte Carlo algorithms with the coarse-grain model are capable of sampling these distributions for any molecular weight in quantitative agreement with predictions from atomistic models. Furthermore, molecular dynamics simulations with well-equilibrated systems at all length-scales support an increased dynamic heterogeneity that is emerging from both intermolecular interactions with the flat surface and intramolecular cooperativity. This study provides a detailed comprehensive picture of polyisoprene on a flat surface and consists of an effort to characterize such systems in atomistic detail. [ABSTRACT FROM AUTHOR]

The molecular factors that govern interfacial interactions between a polymer melt and a solid surface remain largely unclear despite significant progress made in the last years. Simulations are increasingly employed to elucidate these features, however, equilibration and sampling with models of long macromolecules in such heterogeneous systems present significant challenges. In this study, we couple the application of preferential sampling techniques with connectivity-altering Monte Carlo algorithms to explore the configurational characteristics of a polyethylene melt in proximity to a surface and a highly curved nanoparticle. Designed algorithms allow efficient sampling at all length scales of large systems required to avoid finite-size effects. Using detailed atomistic models for the polymer and realistic structures for a silica surface and a fullerene, we find that at the extreme limit where particles are comparable to the polymer Kuhn segment length, curvature penalizes the formation of long train segments. As a result, an increased number of shorter contacts belonging to different chains are made competing with the anticipated decrease of the bound layer thickness with particle size if polymer adsorbed per unit area remained constant. For very small nanoparticles, formation of new train segments cannot compete with the overall reduction of adsorbance which is present irrespective of the enthalpic interactions; a result that demonstrates the need for an accurate description of polymer rigidity at these length scales. [ABSTRACT FROM AUTHOR]

Potential of mean force calculations along a reaction coordinate (RC) demand exhaustive sampling, which often leads to prohibitively long computational times. The expanded ensemble density of states (EXEDOS) [E. B. Kim, R. Faller, Q. Yan et al., J. Chem. Phys. 117, 7781 (2002)] is a simple flat-histogram Monte Carlo method based on the density of states algorithm proposed by Wang and Landau [Phys. Rev. Lett. 86, 2050 (2001)]. EXEDOS offers the advantage of continuous uniform sampling of the RC with no a priori knowledge of the free energy profile. However, the method is not certain to converge within accessible simulation time. Furthermore, the strongly asymmetric distribution of tunneling times inherent in flat-histogram sampling imposes additional limitations. We propose several improvements that accelerate the EXEDOS method and can be generally applicable in free energy calculations. First, we propose an asynchronous parallel implementation of the density of states algorithm in a multiple-walkers multiple-windows scheme and extend the algorithm in an expanded ensemble [(MW)2-XDOS] for PMF calculations as the original EXEDOS. Despite the nonideal scaling over a number of processors this technique overcomes limitations by extreme values of tunneling times and allows consistent evaluations of performance. The second set of improvements addresses the dependence of convergence times on system size, density, and sampling rate of the RC. At low densities, the coupling of (MW)2-XDOS with the rejection-free geometric cluster move provides impressive performance that overshadows any other technique. However, the limited applicability of cluster moves at high densities requires an alternative approach. We propose the coupling of (MW)2-XDOS with preferential sampling methods. In the systems studied, single displacements in the proximity of particles defining the RC accelerate calculations significantly and render the simulation nearly size-independent. A further modification of preferential sampling involves collective displacements of particles performed in a “smart Monte Carlo” scheme. This “local Brownian dynamics” algorithm can be generally applicable to many free energy simulation methods and would be particularly beneficial at high densities and molecular systems with strong intramolecular potentials. [ABSTRACT FROM AUTHOR]