Inverse design of polymer alloys using deep learning based on self-consistent field analysis and finite element analysis

Abstract

In this study, a framework that deals with the inverse design of material structures based on macroscopic properties using deep learning models is proposed. For the framework, “polymer alloy’s phase separation structures” as material structures and “stresses” as macroscopic properties are considered. The results of self-consistent field analysis are used as structure data, in particular, the analysis result of two-dimensional phase separation structures of a diblock copolymer melt in the equilibrium state. For the stress data, the results of finite element analysis are used, in particular, the analysis results of average stresses over the system under the small-enforced-displacement condition. This framework consists of two deep learning models—one aimed at generating phase separation structures, and the other at predicting properties from structures. For the second model, we incorporate a convolutional neural network. By implementing a random search toward a model that combines these two deep learning models, we constructed the framework for the inverse design of phase separation structures that possess desired macroscopic properties. This framework is demonstrated to be able to suggest phase separation structures that realize desired stresses that are input to the framework through examples. The innovation of the method is the development of framework to propose the optimal structure of polymer alloys by using machine learning. The main novelties of this method are the construction of generative adversarial network that reproduces the phase-separation structure of polymer alloy specified by label information, realization of inverse analysis based on mechanical properties, and the simultaneous optimization of multiple mechanical properties.