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

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.