Phase diagrams of polymer-containing liquid mixtures with a theory-embedded neural network

We develop deep neural networks (DNNs) that consider the phase separation of polymeric liquids. In this talk, we discuss our new built-in function that is constructed through coarse-grained mean-field theory and the scaling laws in polymer physics. This characteristic theory-embedded layer enables us to perform the learning process efficiently with relatively small numbers of artificial neurons and hidden layers and provides the DNNs with reasonable predictive power. To demonstrate the efficacy of our DNNs, we will discuss the phase diagrams of polymer solutions, and the salt-free and salt-doped diblock copolymer melts. Moreover, we will show the predictive power of the DNNs by considering some experiments for the lithium salt-doped diblock copolymers such as PEO-b-PS.