A multistep method for steady-state Monte Carlo simulations of polymerization processes

Identifying the microscopic information of polymers is of great significance for polymerization processes. Monte Carlo (MC) simulation is a powerful tool to predict the microscopic structure of polymers. Currently, most MC methods are designed for dynamic polymerization processes based on time evolution. The study on MC simulation for steady-state processes is scarce and current approaches face challenges in addressing complex mechanisms. In this work, a multistep method is proposed for the steady-state MC simulation. By introducing the "buffer pool " concept, the proposed method is computationally efficient and flexible to derive accurate predictions for processes with various polymerization mechanisms. Three applications with increasing complexity in the kinetic mechanisms, including both linear and branching polymerizations, are presented to demonstrate the applicability of the proposed method.