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

## TL;DR

This paper shows that Bayesian optimization can effectively tune complex polymer models, enabling accurate simulations of material properties.

## Contribution

The study demonstrates Bayesian optimization's viability for high-dimensional polymer model parameterization.

## Key findings

- Bayesian optimization with TPE successfully parametrized a 41-parameter Pebax model.
- The optimized model accurately reproduces structural and thermodynamic properties of the atomistic system.
- BO-TPE outperforms traditional methods in convergence speed and accuracy.

## Abstract

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.