MOLECULAR MODELING OF THERMAL AND MECHANICAL PROPERTIES OF ELASTOMERS: A REVIEW

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Machine learning (ML) is having an increasing impact on the physical sciences, engineering, and technology and its integration into molecular simulation frameworks holds great potential to expand their scope of applicability to complex materials and facilitate fundamental knowledge and reliable property predictions, contributing to the development of efficient materials design routes. The application of ML in materials informatics in general, and polymer informatics in particular, has led to interesting results, however great untapped potential lies in the integration of ML techniques into the multiscale molecular simulation methods for the study of macromolecular systems, specifically in the context of Coarse Grained (CG) simulations. In this Perspective, we aim at presenting the pioneering recent research efforts in this direction and discussing how these new ML-based techniques can contribute to critical aspects of the development of multiscale molecular simulation methods for bulk complex chemical systems, especially polymers. Prerequisites for the implementation of such ML-integrated methods and open challenges that need to be met toward the development of general systematic ML-based coarse graining schemes for polymers are discussed.

Fracture in Silica/Butadiene Rubber: A Molecular Dynamics View of Design-Property Relationships

Despite intense investigation, the mechanisms governing the mechanical reinforcement of polymers by dispersed nanoparticles have only been partially clarified. This is especially true for the ultimate properties of the nanocomposites, which depend on their resistance to fracture at large deformations. In this work, we adopt molecular dynamics simulations to investigate the mechanical properties of silica/polybutadiene rubber, using a quasi-atomistic model that allows a meaningful description of bond breaking and fracture over relatively large length scales. The behavior of large nanocomposite models is explored systematically by tuning the cross-linking, grafting densities, and nanoparticle concentration. The simulated stress-strain curves are interpreted by monitoring the breaking of chemical bonds and the formation of voids, up to complete rupture of the systems. We find that some chemical bonds, and particularly the S-S linkages at the rubber-nanoparticle interface, start breaking well before the appearance of macroscopic features of fracture and yield.

Towards realistic simulations of polymer networks: tuning vulcanisation and mechanical properties

Simulations of coarse-grained network models have long been used to test theoretical predictions about rubber elasticity, while atomistic models are still largely unexplored. Here we devise a novel algorithm for the vulcanisation of united-atom poly(cis-1,4-butadiene), characterize the topology of the resulting networks and test their mechanical properties. We observe clear changes in the network structure when using slower vulcanisation, contrary to the traditional view that cross-linking simply freezes the melt configuration. Non-ideality of our networks reverberates on the distribution of strand length and on the strands deformation, which is highly non-affine, especially for short strands. Nevertheless, we do recover some of the trends observed on ideal bead-and-spring networks and controlled laboratory experiments, such as the linear relationships linking the degree of cross-linking and the density. We also compare different deformation methods and find step-equilibrium protocols to be more reliable. Regardless of the adopted method, it is advisable to precede the deformation by a pre-stretching cycle in order to release internal stresses accumulated during the vulcanisation.

All acrylic-based thermoplastic elastomers with high upper service temperature and superior mechanical properties

All acrylic-based thermoplastic elastomers (TPEs) offer potential alternatives to the widely-used styrenic TPEs.

Filler reinforcement in cross-linked elastomer nanocomposites: insights from fully atomistic molecular dynamics simulation

Using a fully atomistic model, we perform large-scale molecular dynamics simulations of sulfur-cured polybutadiene (PB) and nanosilica-filled PB composites. A well-integrated network without sol fraction is built dynamically by cross-linking the coarse-grained precursor chains in the presence of embedded silica nanoparticles. Initial configurations for subsequent atomistic simulations are obtained by reverse mapping of the well-equilibrated coarse-grained systems. Based on the concept of "maximally inflated knot" introduced by Grosberg et al., we show that the networks simulated in this study behave as mechanically isotropic systems. Analysis of the network topology in terms of graph theory reveals that mechanically inactive tree-like structures are the dominant structural components of the weakly cross-linked elastomer, while cycles are mainly responsible for the transmission of mechanical forces through the network. We demonstrate that quantities such as the system density, thermal expansion coefficient, glass transition temperature and initial Young's modulus can be predicted in qualitative and sometimes even in quantitative agreement with experiments. The nano-filled system demonstrates a notable increase in the glass transition temperature and an approximately two-fold increase in the nearly equilibrium value of elastic modulus relative to the unfilled elastomer even at relatively small amounts of filler particles. We also examine the structural rearrangement of the nanocomposite subjected to tensile deformation. Under high strain-rate loading, the formation of structural defects (microcavities) within the polymer bulk is observed. The nucleation and growth of cavities in the post-yielding strain hardening regime mainly take place at the elastomer/nanoparticle interfaces. As a result, the cavities are concentrated just near the embedded nanoparticles. Therefore, while the silica nanofiller increases the elastic modulus of the elastomer, it also creates a more defective structure of higher energy in comparison with the unfilled network.