Coarse-graining in simulations of multicomponent polymer systems

Assessment of the partial saddle point approximation in field-theoretic polymer simulations

Field-theoretic simulations are numerical treatments of polymer field theory models that go beyond the mean-field self-consistent field theory level and have successfully captured a range of mesoscopic phenomena. Inherent in molecularly-based field theories is a "sign problem" associated with complex-valued Hamiltonian functionals. One route to field-theoretic simulations utilizes the complex Langevin (CL) method to importance sample complex-valued field configurations to bypass the sign problem. Although CL is exact in principle, it can be difficult to stabilize in strongly fluctuating systems. An alternate approach for blends or block copolymers with two segment species is to make a "partial saddle point approximation" (PSPA) in which the stiff pressure-like field is constrained to its mean-field value, eliminating the sign problem in the remaining field theory, allowing for traditional (real) sampling methods. The consequences of the PSPA are relatively unknown, and direct comparisons between the two methods are limited. Here, we quantitatively compare thermodynamic observables, order-disorder transitions, and periodic domain sizes predicted by the two approaches for a weakly compressible model of AB diblock copolymers. Using Gaussian fluctuation analysis, we validate our simulation observations, finding that the PSPA incorrectly captures trends in fluctuation corrections to certain thermodynamic observables, microdomain spacing, and location of order-disorder transitions. For incompressible models with contact interactions, we find similar discrepancies between the predictions of CL and PSPA, but these can be minimized by regularization procedures such as Morse calibration. These findings mandate caution in applying the PSPA to broader classes of soft-matter models and systems.

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.

Structural and Mechanical Properties of Ionic Di-block Copolymers via a Molecular Dynamics Approach

Polymerized ionic copolymers have recently evolved as a new class of materials to overcome the limited range of mechanical properties of ionic homopolymers. In this paper, we investigate the structural and mechanical properties of charged ionic homopolymers and di-block copolymers, while using coarse-grained molecular dynamics simulation. Tensile and compressive deformation are applied to the homopolymers and copolymers in the glassy state. The effect of charge ratio and loading direction on the stress-strain behavior are studied. It is found that the electrostatic interactions among charged pairs play major roles, as evidenced by increased Young’s modulus and yield strength with charge ratio. Increased charge ratio lead to enhanced stress contribution from both bonding and pairwise (Van der Waals + coulombic) interaction. The increase in the gyration of the radius is observed with increasing charge ratio in homopolymers, yet a reversed tendency is observed in copolymers. Introduced charge pairs leads to an increased randomness in the segmental orientation in copolymers.

Segmental dynamics in lamellar phases of tapered copolymers

Recent experiments have reported that the lamellar phase of salt-doped tapered copolymers exhibit higher ionic conductivity compared to those seen in similar morphologies of diblock copolymers. Such observations were in turn rationalized by invoking the corresponding glass transition temperature of the segregated copolymers. In this work we report the results of coarse-grained molecular dynamics simulations to identify the mechanisms underlying such characteristics. Explicitly, we probe the combined influences of the degree of segregation and the disparity in mobilities of the segments of the two blocks, upon the local relaxation dynamics of tapered copolymers segregated in lamellar phases. Our results show that the local dynamics of tapered copolymers depend on two independent factors, viz., the degree of segregation of such copolymers relative to their order-disorder transition temperature, and the relative mobilities (glass transition temperatures) of the two blocks. In qualitative correspondence with experiments, we find that for appropriate combinations of mobility ratios and degree of segregation, the lamellar phases of tapered copolymers can exhibit faster local segmental dynamics compared to diblock copolymers.

Computer Simulations of Ion Transport in Polymer Electrolyte Membranes

Understanding the mechanisms and optimizing ion transport in polymer membranes have been the subject of active research for more than three decades. We present an overview of the progress and challenges involved with the modeling and simulation aspects of the ion transport properties of polymer membranes. We are concerned mainly with atomistic and coarser level simulation studies and discuss some salient work in the context of pure binary and single ion conducting polymer electrolytes, polymer nanocomposites, block copolymers, and ionic liquid-based hybrid electrolytes. We conclude with an outlook highlighting future directions.