Inferring the Physics of Structural Evolution of Multicomponent Polymers via Machine-Learning-Accelerated Method  

作　　者：Kai-Hua Zhang Ying Jiang Liang-Shun Zhang 

机构地区：[1]School of Chemistry,Center of Soft Matter Physics and Its Applications,Beihang University,Beijing,100191,China [2]Shanghai Key Laboratory of Advanced Polymeric Materials,School of Materials Science and Engineering,East China University of Science and Technology,Shanghai,200237,China

出　　处：《Chinese Journal of Polymer Science》2023年第9期1377-1385,I0006,共10页高分子科学（英文版）

基　　金：financially supported by the National Natural Science Foundation of China(Nos.22073028,21873029 and 22073004);the Fundamental Research Funds for the Central Universities。

摘　　要：Dynamic self-consistent field theory(DSCFT)is a fruitful approach for modeling the structural evolution and collective kinetics for a wide variety of multicomponent polymers.However,solving a set of DSCFT equations remains daunting because of high computational demand.Herein,a machine learning method,integrating low-dimensional representations of microstructures and long short-term memory neural networks,is used to accelerate the predictions of structural evolution of multicomponent polymers.It is definitively demonstrated that the neural-network-trained surrogate model has the capability to accurately forecast the structural evolution of homopolymer blends as well as diblock copolymers,without the requirement of“on-the-fly”solution of DSCFT equations.Importantly,the data-driven method can also infer the latent growth laws of phase-separated microstructures of multicomponent polymers through simply using a few of time sequences from their past,without the prior knowledge of the governing dynamics.Our study exemplifies how the machine-learning-accelerated method can be applied to understand and discover the physics of structural evolution in the complex polymer systems.

关 键 词：Machine learning Dynamic self-consistent field theory Structural evolution Block copolymers Homopolymer blends 

分 类 号：TQ317[化学工程—高聚物工业] TP181[自动化与计算机技术—控制理论与控制工程]