Synthetic polymers are a broad and versatile class of soft materials covering a large chemical space. “Computational microscopy” approaches such as atomistic molecular dynamics (MD) simulations are an effective tool to validate and rationalize experimental data for structure–property characterization. The predictive quality of MD simulations and the properties derived from them are primarily driven by the accuracy and relevance of the force field used to represent the system. While biomolecular simulation (nucleic acids, proteins) workflows benefit from dedicated toolkits and domain-specific force fields, the modeling of synthetic polymers has not progressed to the same extent. This perspective will discuss recent efforts to improve system building and parametrization workflows for synthetic polymers, and the unique challenges differentiating them from biopolymers. We will outline shortcomings in established workflows, review best practices for FAIR polymer simulations, and highlight new tools/workflows leveraging cheminformatics, direct chemical perception, and neural networks.