Bayesian Optimization for High-Dimensional Coarse-Grained Model Parameterization: A Case Study on Pebax Polymer

Coarse-grained (CG) force field models are extensively utilized in material simulations because of their scalability. Ordinarily, these models are parametrized using hybrid strategies that sequentially integrate top-down and bottom-up approaches. However, this combination restricts the capacity to jointly optimize all parameters. Although Bayesian optimization (BO) has been explored as an alternative search strategy to identify well-optimized CG parameters, its application has conventionally been limited to low-dimensional scenarios. This has contributed to the assumption that BO is unsuitable for more complex CG models, which often involve a large number of parameters. In this study, we challenge this assumption by successfully extending BO, using the tree-structured Parzen estimator (TPE) model, to optimize a high-dimensional CG model. Specifically, we show that a 41-parameter CG model of Pebax-1657, a copolymer composed of alternating polyamide and polyether segments, can be effectively parametrized using BO, resulting in a model that accurately reproduces the key physical properties of its parent atomistic representation. Our optimization framework simultaneously targets structural and thermodynamic properties, namely, density, radius of gyration, and glass transition temperature. Compared to traditional search algorithms, BO-TPE not only converges faster but also delivers consistent improvements over more standard parametrization approaches.