Analytical coarse grained potential parameterization by Reinforcement Learning for anisotropic cellulose

Abstract

Cellulose nanocrystals (CNCs) are a type of cellulose with excellent mechanical performance and other merit attributes. According to previous reports, hydrogen bonds play a pivotal role in the anisotropic structure of the CNC. Understanding the structure and mechanical behavior of CNC on a mesoscopic scale is critical for the development and manufacture of cellulose materials. However, experimental observations and atomistic simulations are not appropriate on the mesoscopic scale. In this study, we introduce an analytical coarse-grained (CG) potential following an extended bottom-up approach that is directly parameterized using Reinforcement Learning (RL). RL is a powerful tool for industrial and academic applications in various fields. Nevertheless, the potential of RL has not yet been fully exploited in the field of molecular dynamics. The RL and Boltzmann inversion methods were employed to develop a novel CG model of cellulose to represent its anisotropy and polymer stiffness. The resultant CG model is not limited to the target properties for training, and can reproduce the dynamics mechanical properties under other circumstances without additional training. This model confirms that RL can construct a CG potential that is both physically explainable and powerful.