Machine Learning‐Assisted Multi‐Target Coarse‐Graining Strategy for Polystyrene

ABSTRACT Coarse‐grained (CG) molecular dynamics offers a powerful means to bridge atomistic simulations and macroscopic experiments, but constructing CG models that simultaneously preserve structural, thermodynamic, and dynamical consistency remains challenging. Here, we present a machine learning‐assisted, multi‐objective parameterization strategy for atactic polystyrene (PS) based on a 2:1 mapping scheme. By integrating Support Vector Regression (SVR) and Particle Swarm Optimization (PSO), we systematically optimize Lennard‐Jones parameters to reproduce atomistic‐level radial distribution functions, density, cohesive energy density, and self‐diffusion coefficients at 600 and 1 . Notably, the inclusion of the diffusion coefficient as an optimization target enables the construction of a dynamically consistent CG model. The resulting CG force field achieves remarkable agreement with all‐atom (AA) simulations across multiple observables, establishing a robust framework for predictive polymer modeling. This methodology provides a framework that could be extended to materials discovery and rational polymer design in future studies.