Monte Carlo simulation of terpolymerization: Optimizing the simulation and post‐processing times

Abstract

AbstractMonte Carlo simulations are a useful and easy way to understand a polymerization reaction process properly. However, achieving reliable results with Monte Carlo simulations can also lead to prohibitive computational times and a considerable amount of data to be processed afterward. The present study analyses the Monte Carlo simulation of a steady‐state terpolymerization process to reduce the overall computational time of the simulation and the post‐processing of its results. Different sorting algorithms (Bubble, Insertion, Selection, and Tim) and Python libraries (Joblib and Numba) were used. The chain composition distribution and the micro‐structures resultant of different scenarios were assessed by processing the simulated mechanism results. The simulation time results indicate the Tim sorting algorithm as the best to use in the post‐processing step and the Numba library as the best suited for both the simulation and the post‐processing step.