Bayesian Optimization in Polymer Modeling: From Coarse‐Graining Foundations to Autonomous Inverse Design

ABSTRACT Coarse‐graining (CG) is crucial for simulating polymers at extended scales, addressing the computational limitations of atomistic molecular dynamics. However, developing accurate and transferable CG force fields remains a formidable challenge, due to high‐dimensional parameter spaces, conflicting objectives, and inherent noise. This review highlights Bayesian Optimization (BO) as a transformative, data‐driven framework for automating the parameterization of CG force fields. BO leverages probabilistic Gaussian process surrogates and acquisition functions to navigate complex landscapes efficiently, minimizing expensive simulations while quantifying uncertainty. We survey BO's application in CG model development, from single‐ to multi‐objective optimization for achieving structural, thermodynamic, and dynamic fidelity, and enhancing transferability across conditions. Key examples include CG models for electrolytes, block copolymers, and epoxy resins, often integrated with advanced machine learning techniques for learning potentials, optimal mappings, and active data acquisition. We also discuss emerging autonomous pipelines like SPACIER, RAPSIDY, CAMELOT, and PAL 2.0, which streamline the inverse design. Finally, we outline persistent challenges such as surrogate scalability, handling nonstationarity, and extending to reactive/multiscale systems, and envision BO as a cornerstone of future automated materials discovery, accelerating the design of novel polymeric materials.