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.
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。
作者简介
Corresponding authors:Ying Jiang,E-mail:yjiang@buaa.edu.cn(Y.J.);Corresponding authors:Liang-Shun Zhang,E-mail:E-mail:zhangls@ecust.edu.cn(L.S.Z.)。
