Issue 24, 2021

Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics

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.

Graphical abstract: Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics

Article information

Article type
Paper
Submitted
09 Mar 2021
Accepted
07 May 2021
First published
10 May 2021

Soft Matter, 2021,17, 5864-5877

Author version available

Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics

Z. Ma, S. Wang, M. Kim, K. Liu, C. Chen and W. Pan, Soft Matter, 2021, 17, 5864 DOI: 10.1039/D1SM00364J

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