Molecular Dynamics Simulations of Ideal Living Polymerization: Terminal Model and Kinetic Aspects

Living polymerization is an important synthetic approach to achieving precise control of synthesized polymers, which is crucial for their applications. The molecular weight distribution (MWD) prescribes the macroscopic properties of polymers and hence is a key feature to characterize polymerization. In this work, we present a systematic molecular dynamics simulation study of ideal living polymerization in bulk and surface-initiated systems based on a terminal stochastic reaction model. The evolution of polymer dispersity and MWD along with the polymerization process is examined. We demonstrate that MWD is generally well captured by the Schulz-Zimm distribution for bulk and surface-initiated systems with low grafting densities. However, as the grafting density in the surface-initiated case increases, heterogeneity in chain growth emerges due to the kinetic trapping of reactive sites, which causes the starving of short chains and the thriving of minority long chains such that a shoulder region shows up in MWD. This effect can be enhanced by kinetic compressing induced by polymerization. In addition, the interplay of bonding reaction kinetics and other kinetic properties (e.g., mass transfer and polymer relaxation) is further explored, alongside the influences of bonding probability and reactant concentration. We expect that this investigation will aid in our understanding of typical kinetic aspects of living polymerization.

Machine Learning-Assisted Identification of Copolymer Microstructures Based on Microscopic Images

The microstructure of polymer materials is an important bridge between their molecular structure and macroproperties, which is of great significance to be effectively identified. With the increasing refinement of polymer material design, the microstructure of different polymer materials gradually converges, which is difficult to distinguish. In this study, the machine learning method is applied to recognize the microstructure. A highly accurate and interpretable model based on small experimental data sets has been completed by the methods of transfer learning and feature visualization, making the result of the model that can be explained from the perspective of physical chemistry. This work provides an idea for identifying microstructure and will help further promote intelligent polymer research and development.