Scalable Bayesian Optimization for High-Dimensional Coarse-Grained Model Parameterization

2025/06/27 by Cruz Junior, Carlos A. Martins Junior, Daniela Andrade Damasceno +12 · 1 voice
Engineering · Materials Science · #Block Copolymer Self-Assembly #Composite Material Mechanics #Machine Learning in Materials Science #cond-mat.mtrl-sci #physics.comp-ph

paper · pdf · doi:10.48550/arxiv.2506.22533

openalex publication_date 2025/06/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Coarse-grained (CG) force field models are extensively utilised in material simulations due to their scalability. Traditionally, these models are parameterized using hybrid strategies that integrate top-down and bottom-up approaches; however, this combination restricts the capacity to jointly optimize all parameters. While Bayesian Optimization (BO) has been explored as an alternative search strategy for identifying optimal parameters, its application has traditionally been limited to low-dimensional problems. This has contributed to the perception that BO is unsuitable for more realistic CG models, which often involve a large number of parameters. In this study, we challenge this assumption by successfully extending BO 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 parameterized using BO, resulting in a model that accurately reproduces key physical properties of its atomistic counterpart. Our optimization framework simultaneously targets density, radius of gyration, and glass transition temperature. It achieves convergence in fewer than 600 iterations, resulting in a CG model that shows consistent improvements across all three properties.

Citations

• Machine Learning Optimization of non-Kasha Behavior and of Transient Dynamics in Model Retinal Isomerization
• Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance
• Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?
• High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces
• Optuna: A Next-generation Hyperparameter Optimization Framework
• Customizing Pareto Simulated Annealing for Multi-objective Optimization of Control Cabinet Layout
• High Dimensional Bayesian Optimisation and Bandits via Additive Models
• Bayesian Optimization in a Billion Dimensions via Random Embeddings
• Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions
• Deriving effective mesoscale potentials from atomistic simulations
• Molecular dynamics simulations of biomolecules
• Taking the Human Out of the Loop: A Review of Bayesian Optimization

Discussions

• 🚀BayesOpt for "large" Coarse-Grained Models! BayesOpt enabled the optimization of a 41-parameter CG model without traditional heuristics or task decomposition. arxiv.org/abs/2506.22533 #compchem #AI4 [bsky, 6 points, 0 comments]

Related

• A novel machine learning enabled hybrid optimization framework for efficient and transferable coarse-graining of a model polymer
• Refining Coarse-Grained Molecular Topologies: A Bayesian Optimization Approach
• Optimization of coarse-grained models: matching probability density in conformational space
• Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains
• Bayesian Optimization in Materials Science: A Survey