A quantitative prediction of polymer-entangled dynamics based on molecular simulation is a grand challenge in contemporary computational material science. The drastic increase of relaxation time and viscosity in high-molecular-weight polymeric fluids essentially limits the usage of classic molecular dynamics simulation. Here, we demonstrate a systematic coarse-graining approach for modeling entangled polymers under the slip-spring particle-field scheme. Specifically, a frequency-controlled slip-spring model, a hybrid particle-field model, and a coarse-grained model of polystyrene melts are combined into a hybrid simulation technique. Via a rigorous parameterization strategy to determine the parameters in slip-springs from existing experimental or simulation data, we show that the reptation behavior is clearly observed in multiple characteristics of polymer dynamics, mean-square displacements, diffusion coefficients, reorientational relaxation, and Rouse mode analysis, consistent with the predictions of the tube theory. All dynamical properties of the slip-spring particle-field models are in good agreement with classic molecular dynamics models. Our work provides an efficient and practical approach to establish chemical-specific coarse-grained models for predicting polymer-entangled dynamics.

We study the compatibilizing effect of copolymers of different architectures on the interface between two incompatible polymer phases by dissipative particle dynamics. Three base polymer systems are investigated, namely weakly incompatible (interspecies repulsion parameter of the dissipative particle dynamics interaction α AB : 25 < α AB < 30), intermediate-incompatible (30 ≤ α AB < 40), and strongly incompatible systems (α AB ≥ 40). We find that the compatibilization efficiency of all regular block copolymers in strongly incompatible systems can be predicted by a power-law function, which contains the Flory−Huggins interaction parameter, the areal concentration, and the mean block length of the compatibilizer. Regular multiblock copolymers have better compatibilization performance compared to the symmetric diblock copolymers at the same areal concentration. This is because smaller amounts of the multiblock copolymer are required to saturate a given interfacial area. For unsymmetric diblock copolymers in strongly incompatible systems, we find additionally that the length of the shortest block is a more important determinant for the compatibilization efficiency than the ratio of block lengths. Our work reveals the involved mechanisms of the compatibilization process, and it provides a promising route to predict the compatibilization efficiency of differently structured copolymer additives in the respective polymer blends.

Polymer sequence engineering is emerging as a potential tool to modulate material properties. Here, we employ a combination of a genetic algorithm (GA) and atomistic molecular dynamics (MD) simulation to design polyethylene−polypropylene (PE−PP) copolymers with the aim of identifying a specific sequence with high thermal conductivity. PE−PP copolymers with various sequences at the same monomer ratio are found to have a broad distribution of thermal conductivities. This indicates that the monomer sequence has a crucial effect on thermal energy transport of the copolymers. A non-periodic and non-intuitive optimal sequence is indeed identified by the GA, which gives the highest thermal conductivity compared with any regular block copolymers, for example, diblock, triblock, and hexablock. In comparison to the bulk density, chain conformations, and vibrational density of states, the monomer sequence has the strongest impact on the efficiency of thermal energy transport via inter-and intra-molecular interactions. Our work highlights polymer sequence engineering as a promising approach for tuning the thermal conductivity of copolymers, and it provides an example application of integrating atomistic MD modeling with the GA for computational material design.

In hybrid particle-field (hPF) simulations (J. Chem. Phys., 2009 130, 214106), the entangled dynamics of polymer melts is lost due to chain crossability. Chains cross, because the field-treatment of the nonbonded interactions makes them effectively soft-core. We introduce a multi-chain slip-spring model (J. Chem. Phys., 2013 138, 104907) into the hPF scheme to mimic the topological constraints of entanglements. The structure of the polymer chains is consistent with that of regular molecular dynamics simulations and is not affected by the introduction of slip-springs. Although slight deviations are seen at short times, dynamical properties such as mean-square displacements and reorientational relaxation times are in good agreement with traditional molecular dynamics simulations and theoretical predictions at long times.

Graft copolymers are widely used as compatibilizers in homopolymer blends. Computational modeling techniques for predicting the compatibilization efficiency of such polymeric materials have substantially accelerated their development. We employ an efficient particle-based simulation method, namely dissipative particle dynamics (DPD), to systematically investigate the compatibilization efficiency of graft copolymers for a wide range of design parameters such as polymer chemistry, backbone and side chain lengths, and the number of side chains. We find that regular graft copolymers (with regular side chain distribution) exhibit different compatibilization efficiencies at the same areal concentrations. This indicates that the molecular architecture plays a critical role in their compatibilization efficiency. To understand these observations, detailed analysis has been performed. Specifically, the relative shape anisometry of the graft copolymers, which is defined as the ratio of their gyration tensor elements in directions normal and parallel to the surface, is found to be strongly correlated to their compatibilization efficiency. Furthermore, we have investigated three specific graft copolymer types, namely, double-end-grafted (side chains concentrated near both chain ends of the backbone), mid-grafted (side chains concentrated on the center of the backbone), and single-end-grafted (side chains only concentrated near one end of the backbone), to understand the influence of varying side chain distributions. Compared to all other series, the mid-grafted copolymers exhibit the best compatibilization efficiency. Combining the obtained DPD results with five models of machine learning (ML), including linear regression (LR), elastic net (EN), random forest (RF), extra tree (ET), and gradient boosting (GB), provides effective predictions for the compatibilization efficiency. The GB model, which yields the best accuracy, has been further used to acquire the feature importance rank (FIR). Starting from these ML models and the FIR analysis, we have developed a framework for fast predictions of the compatibilization efficiency of graft copolymers. This novel framework utilizes physical insights into effects of material properties, such as chemistries and molecular architectures, on the compatibilization efficiency of graft copolymers and paves the way for advanced design of polymer compatibilizers.