Transfer Learning of Memory Kernels in Coarse-grained Modeling

TL;DR

This paper develops transfer learning methods for memory kernels in coarse-grained polymer models, enabling accurate dynamic predictions across different conditions with minimal data.

Contribution

It introduces Gaussian process regression-based transfer learning of memory kernels, improving efficiency and transferability in coarse-grained polymer simulations.

Findings

Transfer learning achieves accurate out-of-sample predictions.

Methods require minimal training data.

Models reproduce dynamics across various thermodynamic conditions.

Abstract

The present work concerns the transferability of coarse-grained (CG) modeling in reproducing the dynamic properties of the reference atomistic systems across a range of parameters. In particular, we focus on implicit-solvent CG modeling of polymer solutions. The CG model is based on the generalized Langevin equation, where the memory kernel plays the critical role in determining the dynamics in all time scales. Thus, we propose methods for transfer learning of memory kernels. The key ingredient of our methods is Gaussian process regression. By integration with the model order reduction via proper orthogonal decomposition and the active learning technique, the transfer learning can be practically efficient and requires minimum training data. Through two example polymer solution systems, we demonstrate the accuracy and efficiency of the proposed transfer learning methods in the construction of transferable memory kernels. The transferability allows for out-of-sample predictions, even in the extrapolated domain of parameters. Built on the transferable memory kernels, the CG models can reproduce the dynamic properties of polymers in all time scales at different thermodynamic conditions (such as temperature and solvent viscosity) and for different systems with varying concentrations and lengths of polymers.