Machine learning (ML) algorithms, through self-learning from preexisting computational or experimental data, have served as powerful tools for designing new molecules, predicting material properties, and understanding biochemical systems . The machine learning-based QSPR models have attracted considerable attention as it allows the development of predictive mathematical relations linking conditional parameters (such as chemical structure, thermodynamic elements) and property of a range of chemical compounds. have developed a framework to predict the glass transition temperature of polymer materials through the integration of molecular dynamics (MD) simulations and cheminformatics approaches. ML also has been used in predicting the thermal conductivity of nanocomposites. The porous graphene with low thermal conductivity was designed by combining MD simulations and ML. Starting from combining ML algorithms and dissipative particle dynamics (DPD) simulation, Zhou and co-workers have developed a framework for rapid prediction of the compatibility efficiency of graft polymers, which is helpful for high-level design of compatibility polymers. These successful combination of molecular dynamics simulations and machine learning illustrate the feasibility of this approach.